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is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16090">arXiv:2410.16090</a> <span> [<a href="https://arxiv.org/pdf/2410.16090">pdf</a>, <a href="https://arxiv.org/format/2410.16090">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Analysing the Residual Stream of Language Models Under Knowledge Conflicts </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhao%2C+Y">Yu Zhao</a>, <a href="/search/cs?searchtype=author&query=Du%2C+X">Xiaotang Du</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+G">Giwon Hong</a>, <a href="/search/cs?searchtype=author&query=Gema%2C+A+P">Aryo Pradipta Gema</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+H">Hongru Wang</a>, <a href="/search/cs?searchtype=author&query=He%2C+X">Xuanli He</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+K">Kam-Fai Wong</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16090v1-abstract-short" style="display: inline;"> Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context. Such conflicts can lead to undesirable model behaviour, such as reliance on outdated or incorrect information. In this work, we investigate whether LLMs can identify knowledge conflicts and whether it is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16090v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16090v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16090v1-abstract-full" style="display: none;"> Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context. Such conflicts can lead to undesirable model behaviour, such as reliance on outdated or incorrect information. In this work, we investigate whether LLMs can identify knowledge conflicts and whether it is possible to know which source of knowledge the model will rely on by analysing the residual stream of the LLM. Through probing tasks, we find that LLMs can internally register the signal of knowledge conflict in the residual stream, which can be accurately detected by probing the intermediate model activations. This allows us to detect conflicts within the residual stream before generating the answers without modifying the input or model parameters. Moreover, we find that the residual stream shows significantly different patterns when the model relies on contextual knowledge versus parametric knowledge to resolve conflicts. This pattern can be employed to estimate the behaviour of LLMs when conflict happens and prevent unexpected answers before producing the answers. Our analysis offers insights into how LLMs internally manage knowledge conflicts and provides a foundation for developing methods to control the knowledge selection processes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16090v1-abstract-full').style.display = 'none'; document.getElementById('2410.16090v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Foundation Model Interventions Workshop @ NeurIPS 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.15999">arXiv:2410.15999</a> <span> [<a href="https://arxiv.org/pdf/2410.15999">pdf</a>, <a href="https://arxiv.org/format/2410.15999">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Steering Knowledge Selection Behaviours in LLMs via SAE-Based Representation Engineering </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zhao%2C+Y">Yu Zhao</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+G">Giwon Hong</a>, <a href="/search/cs?searchtype=author&query=Du%2C+X">Xiaotang Du</a>, <a href="/search/cs?searchtype=author&query=Gema%2C+A+P">Aryo Pradipta Gema</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+H">Hongru Wang</a>, <a href="/search/cs?searchtype=author&query=He%2C+X">Xuanli He</a>, <a href="/search/cs?searchtype=author&query=Wong%2C+K">Kam-Fai Wong</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.15999v2-abstract-short" style="display: inline;"> Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context -- this phenomenon, known as \emph{context-memory knowledge conflicts}, can lead to undesirable model behaviour, such as reliance on outdated or incorrect information. Analysing the internal activations o… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15999v2-abstract-full').style.display = 'inline'; document.getElementById('2410.15999v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.15999v2-abstract-full" style="display: none;"> Large language models (LLMs) can store a significant amount of factual knowledge in their parameters. However, their parametric knowledge may conflict with the information provided in the context -- this phenomenon, known as \emph{context-memory knowledge conflicts}, can lead to undesirable model behaviour, such as reliance on outdated or incorrect information. Analysing the internal activations of LLMs, we find that they can internally register the signals of knowledge conflict at mid-layers. Such signals allow us to detect whether a knowledge conflict occurs and use \emph{inference-time} intervention strategies to resolve it. In this work, we propose \textsc{SpARE}, a \emph{training-free} representation engineering method that uses pre-trained sparse auto-encoders (SAEs) to control the knowledge selection behaviour of LLMs. \textsc{SpARE} identifies the functional features that control the knowledge selection behaviours and applies them to edit the internal activations of LLMs at inference time. Our experimental results show that \textsc{SpARE} can effectively control the usage of either knowledge source to resolve knowledge conflict in open-domain question-answering tasks, surpassing existing representation engineering methods ($+10\%$) as well as contrastive decoding methods ($+15\%$). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15999v2-abstract-full').style.display = 'none'; document.getElementById('2410.15999v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08670">arXiv:2408.08670</a> <span> [<a href="https://arxiv.org/pdf/2408.08670">pdf</a>, <a href="https://arxiv.org/format/2408.08670">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Alvetreti%2C+F">Federico Alvetreti</a>, <a href="/search/cs?searchtype=author&query=Pomponi%2C+J">Jary Pomponi</a>, <a href="/search/cs?searchtype=author&query=Di+Lorenzo%2C+P">Paolo Di Lorenzo</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a>, <a href="/search/cs?searchtype=author&query=Scardapane%2C+S">Simone Scardapane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.08670v1-abstract-short" style="display: inline;"> Recently, foundation models based on Vision Transformers (ViTs) have become widely available. However, their fine-tuning process is highly resource-intensive, and it hinders their adoption in several edge or low-energy applications. To this end, in this paper we introduce an efficient fine-tuning method for ViTs called $\textbf{ALaST}$ (… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08670v1-abstract-full').style.display = 'inline'; document.getElementById('2408.08670v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08670v1-abstract-full" style="display: none;"> Recently, foundation models based on Vision Transformers (ViTs) have become widely available. However, their fine-tuning process is highly resource-intensive, and it hinders their adoption in several edge or low-energy applications. To this end, in this paper we introduce an efficient fine-tuning method for ViTs called $\textbf{ALaST}$ ($\textit{Adaptive Layer Selection Fine-Tuning for Vision Transformers}$) to speed up the fine-tuning process while reducing computational cost, memory load, and training time. Our approach is based on the observation that not all layers are equally critical during fine-tuning, and their importance varies depending on the current mini-batch. Therefore, at each fine-tuning step, we adaptively estimate the importance of all layers and we assign what we call ``compute budgets'' accordingly. Layers that were allocated lower budgets are either trained with a reduced number of input tokens or kept frozen. Freezing a layer reduces the computational cost and memory usage by preventing updates to its weights, while discarding tokens removes redundant data, speeding up processing and reducing memory requirements. We show that this adaptive compute allocation enables a nearly-optimal schedule for distributing computational resources across layers, resulting in substantial reductions in training time (up to 1.5x), FLOPs (up to 2x), and memory load (up to 2x) compared to traditional full fine-tuning approaches. Additionally, it can be successfully combined with other parameter-efficient fine-tuning methods, such as LoRA. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08670v1-abstract-full').style.display = 'none'; document.getElementById('2408.08670v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.14859">arXiv:2407.14859</a> <span> [<a href="https://arxiv.org/pdf/2407.14859">pdf</a>, <a href="https://arxiv.org/format/2407.14859">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> </div> </div> <p class="title is-5 mathjax"> Enhancing High-Energy Particle Physics Collision Analysis through Graph Data Attribution Techniques </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Verdone%2C+A">A. Verdone</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">A. Devoto</a>, <a href="/search/cs?searchtype=author&query=Sebastiani%2C+C">C. Sebastiani</a>, <a href="/search/cs?searchtype=author&query=Carmignani%2C+J">J. Carmignani</a>, <a href="/search/cs?searchtype=author&query=D%27Onofrio%2C+M">M. D'Onofrio</a>, <a href="/search/cs?searchtype=author&query=Giagu%2C+S">S. Giagu</a>, <a href="/search/cs?searchtype=author&query=Scardapane%2C+S">S. Scardapane</a>, <a href="/search/cs?searchtype=author&query=Panella%2C+M">M. Panella</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2407.14859v1-abstract-short" style="display: inline;"> The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, necessitating the use of advanced analysis techniques for analysis. Recent advancements in deep learning, particularly Graph Neural Networks, have shown promising results in addressin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14859v1-abstract-full').style.display = 'inline'; document.getElementById('2407.14859v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.14859v1-abstract-full" style="display: none;"> The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, necessitating the use of advanced analysis techniques for analysis. Recent advancements in deep learning, particularly Graph Neural Networks, have shown promising results in addressing the challenges but remain computationally expensive. The study presented in this paper uses a simulated particle collision dataset to integrate influence analysis inside the graph classification pipeline aiming at improving the accuracy and efficiency of collision event prediction tasks. By using a Graph Neural Network for initial training, we applied a gradient-based data influence method to identify influential training samples and then we refined the dataset by removing non-contributory elements: the model trained on this new reduced dataset can achieve good performances at a reduced computational cost. The method is completely agnostic to the specific influence method: different influence modalities can be easily integrated into our methodology. Moreover, by analyzing the discarded elements we can provide further insights about the event classification task. The novelty of integrating data attribution techniques together with Graph Neural Networks in high-energy physics tasks can offer a robust solution for managing large-scale data problems, capturing critical patterns, and maximizing accuracy across several high-data demand domains. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.14859v1-abstract-full').style.display = 'none'; document.getElementById('2407.14859v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 6 figures, 2 tables</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.11430">arXiv:2406.11430</a> <span> [<a href="https://arxiv.org/pdf/2406.11430">pdf</a>, <a href="https://arxiv.org/format/2406.11430">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+Y">Yu Zhao</a>, <a href="/search/cs?searchtype=author&query=Scardapane%2C+S">Simone Scardapane</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.11430v4-abstract-short" style="display: inline;"> The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase. Existing approaches to reduce the KV cache size involve either fine-tuning the model to learn a compression strategy or leveraging attention scores to reduce the sequence length. We analyse the attention distributions in decoder-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11430v4-abstract-full').style.display = 'inline'; document.getElementById('2406.11430v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.11430v4-abstract-full" style="display: none;"> The deployment of large language models (LLMs) is often hindered by the extensive memory requirements of the Key-Value (KV) cache, especially as context lengths increase. Existing approaches to reduce the KV cache size involve either fine-tuning the model to learn a compression strategy or leveraging attention scores to reduce the sequence length. We analyse the attention distributions in decoder-only Transformers-based models and observe that attention allocation patterns stay consistent across most layers. Surprisingly, we find a clear correlation between the $L_2$ and the attention scores over cached KV pairs, where a low $L_2$ of a key embedding usually leads to a high attention score during decoding. This finding indicates that the influence of a KV pair is potentially determined by the key embedding itself before being queried. Based on this observation, we compress the KV cache based on the $L_2$ of key embeddings. Our experimental results show that this simple strategy can reduce the KV cache size by 50% on language modelling and needle-in-a-haystack tasks and 90% on passkey retrieval tasks without losing accuracy. Moreover, without relying on the attention scores, this approach remains compatible with FlashAttention, enabling broader applicability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.11430v4-abstract-full').style.display = 'none'; document.getElementById('2406.11430v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">This is an extended version of a paper published in the proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP 2024); this version was presented at the 4th NeurIPS Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.04127">arXiv:2406.04127</a> <span> [<a href="https://arxiv.org/pdf/2406.04127">pdf</a>, <a href="https://arxiv.org/format/2406.04127">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Are We Done with MMLU? </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gema%2C+A+P">Aryo Pradipta Gema</a>, <a href="/search/cs?searchtype=author&query=Leang%2C+J+O+J">Joshua Ong Jun Leang</a>, <a href="/search/cs?searchtype=author&query=Hong%2C+G">Giwon Hong</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Mancino%2C+A+C+M">Alberto Carlo Maria Mancino</a>, <a href="/search/cs?searchtype=author&query=Saxena%2C+R">Rohit Saxena</a>, <a href="/search/cs?searchtype=author&query=He%2C+X">Xuanli He</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+Y">Yu Zhao</a>, <a href="/search/cs?searchtype=author&query=Du%2C+X">Xiaotang Du</a>, <a href="/search/cs?searchtype=author&query=Madani%2C+M+R+G">Mohammad Reza Ghasemi Madani</a>, <a href="/search/cs?searchtype=author&query=Barale%2C+C">Claire Barale</a>, <a href="/search/cs?searchtype=author&query=McHardy%2C+R">Robert McHardy</a>, <a href="/search/cs?searchtype=author&query=Harris%2C+J">Joshua Harris</a>, <a href="/search/cs?searchtype=author&query=Kaddour%2C+J">Jean Kaddour</a>, <a href="/search/cs?searchtype=author&query=van+Krieken%2C+E">Emile van Krieken</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.04127v2-abstract-short" style="display: inline;"> Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs. For example, we find that 57% of the analysed questions in the Virology subset contain errors. To address this issue, we introduce a comprehensive fr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04127v2-abstract-full').style.display = 'inline'; document.getElementById('2406.04127v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.04127v2-abstract-full" style="display: none;"> Maybe not. We identify and analyse errors in the popular Massive Multitask Language Understanding (MMLU) benchmark. Even though MMLU is widely adopted, our analysis demonstrates numerous ground truth errors that obscure the true capabilities of LLMs. For example, we find that 57% of the analysed questions in the Virology subset contain errors. To address this issue, we introduce a comprehensive framework for identifying dataset errors using a novel error taxonomy. Then, we create MMLU-Redux, which is a subset of 3,000 manually re-annotated questions across 30 MMLU subjects. Using MMLU-Redux, we demonstrate significant discrepancies with the model performance metrics that were originally reported. Our results strongly advocate for revising MMLU's error-ridden questions to enhance its future utility and reliability as a benchmark. Therefore, we open up MMLU-Redux for additional annotation https://huggingface.co/datasets/edinburgh-dawg/mmlu-redux. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.04127v2-abstract-full').style.display = 'none'; document.getElementById('2406.04127v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.02330">arXiv:2405.02330</a> <span> [<a href="https://arxiv.org/pdf/2405.02330">pdf</a>, <a href="https://arxiv.org/format/2405.02330">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Information Theory">cs.IT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Semantic Token Selection for AI-native Goal-oriented Communications </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Petruzzi%2C+S">Simone Petruzzi</a>, <a href="/search/cs?searchtype=author&query=Pomponi%2C+J">Jary Pomponi</a>, <a href="/search/cs?searchtype=author&query=Di+Lorenzo%2C+P">Paolo Di Lorenzo</a>, <a href="/search/cs?searchtype=author&query=Scardapane%2C+S">Simone Scardapane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.02330v1-abstract-short" style="display: inline;"> In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation. Transformers have become the standard architecture for pretraining large-scale vision and text models, and preliminary results have shown promising performance also in deep joint source-channel coding (JSCC). H… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02330v1-abstract-full').style.display = 'inline'; document.getElementById('2405.02330v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.02330v1-abstract-full" style="display: none;"> In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation. Transformers have become the standard architecture for pretraining large-scale vision and text models, and preliminary results have shown promising performance also in deep joint source-channel coding (JSCC). Here, we consider a dynamic model where communication happens over a channel with variable latency and bandwidth constraints. Leveraging recent works on conditional computation, we exploit the structure of the transformer blocks and the multihead attention operator to design a trainable semantic token selection mechanism that learns to select relevant tokens (e.g., image patches) from the input signal. This is done dynamically, on a per-input basis, with a rate that can be chosen as an additional input by the user. We show that our model improves over state-of-the-art token selection mechanisms, exhibiting high accuracy for a wide range of latency and bandwidth constraints, without the need for deploying multiple architectures tailored to each constraint. Last, but not least, the proposed token selection mechanism helps extract powerful semantics that are easy to understand and explain, paving the way for interpretable-by-design models for the next generation of AI-native communication systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.02330v1-abstract-full').style.display = 'none'; document.getElementById('2405.02330v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 94A40 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.07965">arXiv:2403.07965</a> <span> [<a href="https://arxiv.org/pdf/2403.07965">pdf</a>, <a href="https://arxiv.org/format/2403.07965">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.3233/IA-240035">10.3233/IA-240035 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Conditional computation in neural networks: principles and research trends </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Scardapane%2C+S">Simone Scardapane</a>, <a href="/search/cs?searchtype=author&query=Baiocchi%2C+A">Alessandro Baiocchi</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Marsocci%2C+V">Valerio Marsocci</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a>, <a href="/search/cs?searchtype=author&query=Pomponi%2C+J">Jary Pomponi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.07965v2-abstract-short" style="display: inline;"> This article summarizes principles and ideas from the emerging area of applying \textit{conditional computation} methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts of their computational graph conditionally on their input. Examples include the dynamic selection of, e.g., input tokens, layers (or sets of layers), a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07965v2-abstract-full').style.display = 'inline'; document.getElementById('2403.07965v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.07965v2-abstract-full" style="display: none;"> This article summarizes principles and ideas from the emerging area of applying \textit{conditional computation} methods to the design of neural networks. In particular, we focus on neural networks that can dynamically activate or de-activate parts of their computational graph conditionally on their input. Examples include the dynamic selection of, e.g., input tokens, layers (or sets of layers), and sub-modules inside each layer (e.g., channels in a convolutional filter). We first provide a general formalism to describe these techniques in an uniform way. Then, we introduce three notable implementations of these principles: mixture-of-experts (MoEs) networks, token selection mechanisms, and early-exit neural networks. The paper aims to provide a tutorial-like introduction to this growing field. To this end, we analyze the benefits of these modular designs in terms of efficiency, explainability, and transfer learning, with a focus on emerging applicative areas ranging from automated scientific discovery to semantic communication. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.07965v2-abstract-full').style.display = 'none'; document.getElementById('2403.07965v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 8 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Intelligenza Artificiale, vol. Pre-press, pp. 1-16, 2024 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.01262">arXiv:2402.01262</a> <span> [<a href="https://arxiv.org/pdf/2402.01262">pdf</a>, <a href="https://arxiv.org/format/2402.01262">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Class incremental learning with probability dampening and cascaded gated classifier </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pomponi%2C+J">Jary Pomponi</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Scardapane%2C+S">Simone Scardapane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.01262v3-abstract-short" style="display: inline;"> Humans are capable of acquiring new knowledge and transferring learned knowledge into different domains, incurring a small forgetting. The same ability, called Continual Learning, is challenging to achieve when operating with neural networks due to the forgetting affecting past learned tasks when learning new ones. This forgetting can be mitigated by replaying stored samples from past tasks, but a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.01262v3-abstract-full').style.display = 'inline'; document.getElementById('2402.01262v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.01262v3-abstract-full" style="display: none;"> Humans are capable of acquiring new knowledge and transferring learned knowledge into different domains, incurring a small forgetting. The same ability, called Continual Learning, is challenging to achieve when operating with neural networks due to the forgetting affecting past learned tasks when learning new ones. This forgetting can be mitigated by replaying stored samples from past tasks, but a large memory size may be needed for long sequences of tasks; moreover, this could lead to overfitting on saved samples. In this paper, we propose a novel regularisation approach and a novel incremental classifier called, respectively, Margin Dampening and Cascaded Scaling Classifier. The first combines a soft constraint and a knowledge distillation approach to preserve past learned knowledge while allowing the model to learn new patterns effectively. The latter is a gated incremental classifier, helping the model modify past predictions without directly interfering with them. This is achieved by modifying the output of the model with auxiliary scaling functions. We empirically show that our approach performs well on multiple benchmarks against well-established baselines, and we also study each component of our proposal and how the combinations of such components affect the final results. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.01262v3-abstract-full').style.display = 'none'; document.getElementById('2402.01262v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 2 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Previously called "Cascaded Scaling Classifier: class incremental learning with probability scaling ". The official code is available https://github.com/jaryP/CIL-Margin-Dampening-Gated-Classifier</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.10193">arXiv:2312.10193</a> <span> [<a href="https://arxiv.org/pdf/2312.10193">pdf</a>, <a href="https://arxiv.org/format/2312.10193">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=W%C3%B3jcik%2C+B">Bartosz W贸jcik</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Alessio Devoto</a>, <a href="/search/cs?searchtype=author&query=Pustelnik%2C+K">Karol Pustelnik</a>, <a href="/search/cs?searchtype=author&query=Minervini%2C+P">Pasquale Minervini</a>, <a href="/search/cs?searchtype=author&query=Scardapane%2C+S">Simone Scardapane</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.10193v1-abstract-short" style="display: inline;"> The computational cost of transformer models makes them inefficient in low-latency or low-power applications. While techniques such as quantization or linear attention can reduce the computational load, they may incur a reduction in accuracy. In addition, globally reducing the cost for all inputs may be sub-optimal. We observe that for each layer, the full width of the layer may be needed only for… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10193v1-abstract-full').style.display = 'inline'; document.getElementById('2312.10193v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.10193v1-abstract-full" style="display: none;"> The computational cost of transformer models makes them inefficient in low-latency or low-power applications. While techniques such as quantization or linear attention can reduce the computational load, they may incur a reduction in accuracy. In addition, globally reducing the cost for all inputs may be sub-optimal. We observe that for each layer, the full width of the layer may be needed only for a small subset of tokens inside a batch and that the "effective" width needed to process a token can vary from layer to layer. Motivated by this observation, we introduce the Adaptive Computation Module (ACM), a generic module that dynamically adapts its computational load to match the estimated difficulty of the input on a per-token basis. An ACM consists of a sequence of learners that progressively refine the output of their preceding counterparts. An additional gating mechanism determines the optimal number of learners to execute for each token. We also describe a distillation technique to replace any pre-trained model with an "ACMized" variant. The distillation phase is designed to be highly parallelizable across layers while being simple to plug-and-play into existing networks. Our evaluation of transformer models in computer vision and speech recognition demonstrates that substituting layers with ACMs significantly reduces inference costs without degrading the downstream accuracy for a wide interval of user-defined budgets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10193v1-abstract-full').style.display = 'none'; document.getElementById('2312.10193v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2302.02064">arXiv:2302.02064</a> <span> [<a href="https://arxiv.org/pdf/2302.02064">pdf</a>, <a href="https://arxiv.org/format/2302.02064">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Lived Experience Matters: Automatic Detection of Stigma on Social Media Toward People Who Use Substances </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Giorgi%2C+S">Salvatore Giorgi</a>, <a href="/search/cs?searchtype=author&query=Bellew%2C+D">Douglas Bellew</a>, <a href="/search/cs?searchtype=author&query=Habib%2C+D+R+S">Daniel Roy Sadek Habib</a>, <a href="/search/cs?searchtype=author&query=Sherman%2C+G">Garrick Sherman</a>, <a href="/search/cs?searchtype=author&query=Sedoc%2C+J">Joao Sedoc</a>, <a href="/search/cs?searchtype=author&query=Smitterberg%2C+C">Chase Smitterberg</a>, <a href="/search/cs?searchtype=author&query=Devoto%2C+A">Amanda Devoto</a>, <a href="/search/cs?searchtype=author&query=Himelein-Wachowiak%2C+M">McKenzie Himelein-Wachowiak</a>, <a href="/search/cs?searchtype=author&query=Curtis%2C+B">Brenda Curtis</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2302.02064v2-abstract-short" style="display: inline;"> Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02064v2-abstract-full').style.display = 'inline'; document.getElementById('2302.02064v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2302.02064v2-abstract-full" style="display: none;"> Stigma toward people who use substances (PWUS) is a leading barrier to seeking treatment.Further, those in treatment are more likely to drop out if they experience higher levels of stigmatization. While related concepts of hate speech and toxicity, including those targeted toward vulnerable populations, have been the focus of automatic content moderation research, stigma and, in particular, people who use substances have not. This paper explores stigma toward PWUS using a data set of roughly 5,000 public Reddit posts. We performed a crowd-sourced annotation task where workers are asked to annotate each post for the presence of stigma toward PWUS and answer a series of questions related to their experiences with substance use. Results show that workers who use substances or know someone with a substance use disorder are more likely to rate a post as stigmatizing. Building on this, we use a supervised machine learning framework that centers workers with lived substance use experience to label each Reddit post as stigmatizing. Modeling person-level demographics in addition to comment-level language results in a classification accuracy (as measured by AUC) of 0.69 -- a 17% increase over modeling language alone. Finally, we explore the linguist cues which distinguish stigmatizing content: PWUS substances and those who don't agree that language around othering ("people", "they") and terms like "addict" are stigmatizing, while PWUS (as opposed to those who do not) find discussions around specific substances more stigmatizing. Our findings offer insights into the nature of perceived stigma in substance use. Additionally, these results further establish the subjective nature of such machine learning tasks, highlighting the need for understanding their social contexts. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2302.02064v2-abstract-full').style.display = 'none'; document.getElementById('2302.02064v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 July, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 February, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Accepted for publication the 2024 International AAAI Conference on Web and Social Media (ICWSM)</span> </p> </li> </ol> <div 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