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<button class="button 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/2410.11674">arXiv:2410.11674</a> <span> [<a href="https://arxiv.org/pdf/2410.11674">pdf</a>, <a href="https://arxiv.org/format/2410.11674">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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md Kowsher</a>, <a href="/search/cs?searchtype=author&query=Sobuj%2C+M+S+I">Md. Shohanur Islam Sobuj</a>, <a href="/search/cs?searchtype=author&query=Prottasha%2C+N+J">Nusrat Jahan Prottasha</a>, <a href="/search/cs?searchtype=author&query=Alanis%2C+E+A">E. Alejandro Alanis</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</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="2410.11674v1-abstract-short" style="display: inline;"> Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the da… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11674v1-abstract-full').style.display = 'inline'; document.getElementById('2410.11674v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.11674v1-abstract-full" style="display: none;"> Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and univariate datasets demonstrate that LLM-Mixer achieves competitive performance, outperforming recent state-of-the-art models across various forecasting horizons. This work highlights the potential of combining multiscale analysis and LLMs for effective and scalable time-series forecasting. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.11674v1-abstract-full').style.display = 'none'; document.getElementById('2410.11674v1-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 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">Time series forecasting using LLMs</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.10075">arXiv:2410.10075</a> <span> [<a href="https://arxiv.org/pdf/2410.10075">pdf</a>, <a href="https://arxiv.org/format/2410.10075">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"> RoCoFT: Efficient Finetuning of Large Language Models with Row-Column Updates </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md Kowsher</a>, <a href="/search/cs?searchtype=author&query=Esmaeilbeig%2C+T">Tara Esmaeilbeig</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+C">Chun-Nam Yu</a>, <a href="/search/cs?searchtype=author&query=Soltanalian%2C+M">Mojtaba Soltanalian</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</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="2410.10075v2-abstract-short" style="display: inline;"> We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-size LMs like BERT and RoBERTa, and larger LMs like Bloom-7B, Llama2-7B, and Llama2-13B, we show that our method gives comparable or better accuracies than state-of-art PEFT… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10075v2-abstract-full').style.display = 'inline'; document.getElementById('2410.10075v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.10075v2-abstract-full" style="display: none;"> We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-size LMs like BERT and RoBERTa, and larger LMs like Bloom-7B, Llama2-7B, and Llama2-13B, we show that our method gives comparable or better accuracies than state-of-art PEFT methods while also being more memory and computation-efficient. We also study the reason behind the effectiveness of our method with tools from neural tangent kernel theory. We empirically demonstrate that our kernel, constructed using a restricted set of row and column parameters, are numerically close to the full-parameter kernel and gives comparable classification performance. Ablation studies are conducted to investigate the impact of different algorithmic choices, including the selection strategy for rows and columns as well as the optimal rank for effective implementation of our method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.10075v2-abstract-full').style.display = 'none'; document.getElementById('2410.10075v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 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">RoCoFT is a parameter-efficient method</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.08598">arXiv:2410.08598</a> <span> [<a href="https://arxiv.org/pdf/2410.08598">pdf</a>, <a href="https://arxiv.org/format/2410.08598">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"> Parameter-Efficient Fine-Tuning of Large Language Models using Semantic Knowledge Tuning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Prottasha%2C+N+J">Nusrat Jahan Prottasha</a>, <a href="/search/cs?searchtype=author&query=Mahmud%2C+A">Asif Mahmud</a>, <a href="/search/cs?searchtype=author&query=Sobuj%2C+M+S+I">Md. Shohanur Islam Sobuj</a>, <a href="/search/cs?searchtype=author&query=Bhat%2C+P">Prakash Bhat</a>, <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md Kowsher</a>, <a href="/search/cs?searchtype=author&query=Yousefi%2C+N">Niloofar Yousefi</a>, <a href="/search/cs?searchtype=author&query=Garibay%2C+O+O">Ozlem Ozmen Garibay</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="2410.08598v1-abstract-short" style="display: inline;"> Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning and require extensive training for best performance, often falling short. In this context, we propose a novel method called Semantic Knowledge Tuning… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08598v1-abstract-full').style.display = 'inline'; document.getElementById('2410.08598v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.08598v1-abstract-full" style="display: none;"> Large Language Models (LLMs) are gaining significant popularity in recent years for specialized tasks using prompts due to their low computational cost. Standard methods like prefix tuning utilize special, modifiable tokens that lack semantic meaning and require extensive training for best performance, often falling short. In this context, we propose a novel method called Semantic Knowledge Tuning (SK-Tuning) for prompt and prefix tuning that employs meaningful words instead of random tokens. This method involves using a fixed LLM to understand and process the semantic content of the prompt through zero-shot capabilities. Following this, it integrates the processed prompt with the input text to improve the model's performance on particular tasks. Our experimental results show that SK-Tuning exhibits faster training times, fewer parameters, and superior performance on tasks such as text classification and understanding compared to other tuning methods. This approach offers a promising method for optimizing the efficiency and effectiveness of LLMs in processing language tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.08598v1-abstract-full').style.display = 'none'; document.getElementById('2410.08598v1-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> 11 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">Accepted in Nature Scientific Reports</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.10927">arXiv:2409.10927</a> <span> [<a href="https://arxiv.org/pdf/2409.10927">pdf</a>, <a href="https://arxiv.org/format/2409.10927">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"> Propulsion: Steering LLM with Tiny Fine-Tuning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md Kowsher</a>, <a href="/search/cs?searchtype=author&query=Prottasha%2C+N+J">Nusrat Jahan Prottasha</a>, <a href="/search/cs?searchtype=author&query=Bhat%2C+P">Prakash Bhat</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="2409.10927v2-abstract-short" style="display: inline;"> The rapid advancements in Large Language Models (LLMs) have revolutionized natural language processing (NLP) and related fields. However, fine-tuning these models for specific tasks remains computationally expensive and risks degrading pre-learned features. To address these challenges, we propose Propulsion, a novel parameter efficient fine-tuning (PEFT) method designed to optimize task-specific p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10927v2-abstract-full').style.display = 'inline'; document.getElementById('2409.10927v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.10927v2-abstract-full" style="display: none;"> The rapid advancements in Large Language Models (LLMs) have revolutionized natural language processing (NLP) and related fields. However, fine-tuning these models for specific tasks remains computationally expensive and risks degrading pre-learned features. To address these challenges, we propose Propulsion, a novel parameter efficient fine-tuning (PEFT) method designed to optimize task-specific performance while drastically reducing computational overhead. Inspired by the concept of controlled adjustments in physical motion, Propulsion selectively re-scales specific dimensions of a pre-trained model, guiding output predictions toward task objectives without modifying the model's parameters. By introducing lightweight, trainable Propulsion parameters at the pre-trained layer, we minimize the number of parameters updated during fine-tuning, preventing overfitting or overwriting of existing knowledge. Our theoretical analysis, supported by Neural Tangent Kernel (NTK) theory, shows that Propulsion approximates the performance of full fine-tuning with far fewer trainable parameters. Empirically, Propulsion reduces the parameter count from 355.3 million to just 0.086 million, achieving over a 10x reduction compared to standard approaches like LoRA while maintaining competitive performance across benchmarks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.10927v2-abstract-full').style.display = 'none'; document.getElementById('2409.10927v2-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> 18 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 17 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 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">26 pages, 11 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.02402">arXiv:2404.02402</a> <span> [<a href="https://arxiv.org/pdf/2404.02402">pdf</a>, <a href="https://arxiv.org/format/2404.02402">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> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</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"> Token Trails: Navigating Contextual Depths in Conversational AI with ChatLLM </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md. Kowsher</a>, <a href="/search/cs?searchtype=author&query=Panditi%2C+R">Ritesh Panditi</a>, <a href="/search/cs?searchtype=author&query=Prottasha%2C+N+J">Nusrat Jahan Prottasha</a>, <a href="/search/cs?searchtype=author&query=Bhat%2C+P">Prakash Bhat</a>, <a href="/search/cs?searchtype=author&query=Bairagi%2C+A+K">Anupam Kumar Bairagi</a>, <a href="/search/cs?searchtype=author&query=Arefin%2C+M+S">Mohammad Shamsul Arefin</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="2404.02402v1-abstract-short" style="display: inline;"> Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utter… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02402v1-abstract-full').style.display = 'inline'; document.getElementById('2404.02402v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.02402v1-abstract-full" style="display: none;"> Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utterances and bot responses, facilitating the generation of context-aware replies. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of Token Trails in improving conversational understanding and response generation, achieving state-of-the-art performance. Our results highlight the significance of contextual modeling in conversational AI and underscore the promising potential of Token Trails to advance the field, paving the way for more sophisticated and contextually aware chatbot interactions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.02402v1-abstract-full').style.display = 'none'; document.getElementById('2404.02402v1-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> 2 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.09573">arXiv:2402.09573</a> <span> [<a href="https://arxiv.org/pdf/2402.09573">pdf</a>, <a href="https://arxiv.org/format/2402.09573">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="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Changes by Butterflies: Farsighted Forecasting with Group Reservoir Transformer </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md Kowsher</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+A+R">Abdul Rafae Khan</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+J">Jia Xu</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="2402.09573v2-abstract-short" style="display: inline;"> In Chaos, a minor divergence between two initial conditions exhibits exponential amplification over time, leading to far-away outcomes, known as the butterfly effect. Thus, the distant future is full of uncertainty and hard to forecast. We introduce Group Reservoir Transformer to predict long-term events more accurately and robustly by overcoming two challenges in Chaos: (1) the extensive historic… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.09573v2-abstract-full').style.display = 'inline'; document.getElementById('2402.09573v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.09573v2-abstract-full" style="display: none;"> In Chaos, a minor divergence between two initial conditions exhibits exponential amplification over time, leading to far-away outcomes, known as the butterfly effect. Thus, the distant future is full of uncertainty and hard to forecast. We introduce Group Reservoir Transformer to predict long-term events more accurately and robustly by overcoming two challenges in Chaos: (1) the extensive historical sequences and (2) the sensitivity to initial conditions. A reservoir is attached to a Transformer to efficiently handle arbitrarily long historical lengths, with an extension of a group of reservoirs to reduce the sensitivity to the initialization variations. Our architecture consistently outperforms state-of-the-art models in multivariate time series, including TimeLLM, GPT2TS, PatchTST, DLinear, TimeNet, and the baseline Transformer, with an error reduction of up to -59\% in various fields such as ETTh, ETTm, and air quality, demonstrating that an ensemble of butterfly learning can improve the adequacy and certainty of event prediction, despite of the traveling time to the unknown future. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.09573v2-abstract-full').style.display = 'none'; document.getElementById('2402.09573v2-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> 13 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.01643">arXiv:2402.01643</a> <span> [<a href="https://arxiv.org/pdf/2402.01643">pdf</a>, <a href="https://arxiv.org/format/2402.01643">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> <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"> L-TUNING: Synchronized Label Tuning for Prompt and Prefix in LLMs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md. Kowsher</a>, <a href="/search/cs?searchtype=author&query=Sobuj%2C+M+S+I">Md. Shohanur Islam Sobuj</a>, <a href="/search/cs?searchtype=author&query=Mahmud%2C+A">Asif Mahmud</a>, <a href="/search/cs?searchtype=author&query=Prottasha%2C+N+J">Nusrat Jahan Prottasha</a>, <a href="/search/cs?searchtype=author&query=Bhat%2C+P">Prakash Bhat</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="2402.01643v2-abstract-short" style="display: inline;"> Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing. Traditional methods, like prompt or prefix tuning, typically rely on arbitrary tokens for training, leading to prolonged training times and generalized token use across various class labels. To address these issues, this paper introduces L-Tuning, an efficient f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.01643v2-abstract-full').style.display = 'inline'; document.getElementById('2402.01643v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.01643v2-abstract-full" style="display: none;"> Efficiently fine-tuning Large Language Models (LLMs) for specific tasks presents a considerable challenge in natural language processing. Traditional methods, like prompt or prefix tuning, typically rely on arbitrary tokens for training, leading to prolonged training times and generalized token use across various class labels. To address these issues, this paper introduces L-Tuning, an efficient fine-tuning approach designed for classification tasks within the Natural Language Inference (NLI) framework. Diverging from conventional methods, L-Tuning focuses on the fine-tuning of label tokens processed through a pre-trained LLM, thereby harnessing its pre-existing semantic knowledge. This technique not only improves the fine-tuning accuracy and efficiency but also facilitates the generation of distinct label embeddings for each class, enhancing the model's training nuance. Our experimental results indicate a significant improvement in training efficiency and classification accuracy with L-Tuning compared to traditional approaches, marking a promising advancement in fine-tuning LLMs for complex language tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.01643v2-abstract-full').style.display = 'none'; document.getElementById('2402.01643v2-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> 12 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 December, 2023; <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">Published in the ICLR TinyPaper track</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2301.03093">arXiv:2301.03093</a> <span> [<a href="https://arxiv.org/pdf/2301.03093">pdf</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 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/10.1109/ICCIT48885.2019.9038574">10.1109/ICCIT48885.2019.9038574 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Prognosis and Treatment Prediction of Type-2 Diabetes Using Deep Neural Network and Machine Learning Classifiers </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md. Kowsher</a>, <a href="/search/cs?searchtype=author&query=Turaba%2C+M+Y">Mahbuba Yesmin Turaba</a>, <a href="/search/cs?searchtype=author&query=Sajed%2C+T">Tanvir Sajed</a>, <a href="/search/cs?searchtype=author&query=Rahman%2C+M+M+M">M M Mahabubur Rahman</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="2301.03093v1-abstract-short" style="display: inline;"> Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to imbalanced insulin activity.The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with high accuracy,in order to identify and treat diabetes patients at an early age.Our training and test dataset is… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.03093v1-abstract-full').style.display = 'inline'; document.getElementById('2301.03093v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2301.03093v1-abstract-full" style="display: none;"> Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to imbalanced insulin activity.The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with high accuracy,in order to identify and treat diabetes patients at an early age.Our training and test dataset is an accumulation of 9483 diabetes patients information.The training dataset is large enough to negate overfitting and provide for highly accurate test performance.We use performance measures such as accuracy and precision to find out the best algorithm deep ANN which outperforms with 95.14% accuracy among all other tested machine learning classifiers.We hope our high-performing model can be used by hospitals to predict diabetes and drive research into more accurate prediction models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2301.03093v1-abstract-full').style.display = 'none'; document.getElementById('2301.03093v1-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 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2211.02263">arXiv:2211.02263</a> <span> [<a href="https://arxiv.org/pdf/2211.02263">pdf</a>, <a href="https://arxiv.org/format/2211.02263">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> <p class="title is-5 mathjax"> Impact Learning: A Learning Method from Features Impact and Competition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Prottasha%2C+N+J">Nusrat Jahan Prottasha</a>, <a href="/search/cs?searchtype=author&query=Murad%2C+S+A">Saydul Akbar Murad</a>, <a href="/search/cs?searchtype=author&query=Muzahid%2C+A+J+M">Abu Jafar Md Muzahid</a>, <a href="/search/cs?searchtype=author&query=Rana%2C+M">Masud Rana</a>, <a href="/search/cs?searchtype=author&query=Kowsher%2C+M">Md Kowsher</a>, <a href="/search/cs?searchtype=author&query=Adhikary%2C+A">Apurba Adhikary</a>, <a href="/search/cs?searchtype=author&query=Biswas%2C+S">Sujit Biswas</a>, <a href="/search/cs?searchtype=author&query=Bairagi%2C+A+K">Anupam Kumar Bairagi</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="2211.02263v1-abstract-short" style="display: inline;"> Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze da… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.02263v1-abstract-full').style.display = 'inline'; document.getElementById('2211.02263v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2211.02263v1-abstract-full" style="display: none;"> Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2211.02263v1-abstract-full').style.display = 'none'; document.getElementById('2211.02263v1-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> 4 November, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2022. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a 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