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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/2412.04403">arXiv:2412.04403</a> <span> [<a href="https://arxiv.org/pdf/2412.04403">pdf</a>, <a href="https://arxiv.org/format/2412.04403">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"> Establishing Task Scaling Laws via Compute-Efficient Model Ladders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhagia%2C+A">Akshita Bhagia</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J">Jiacheng Liu</a>, <a href="/search/cs?searchtype=author&query=Wettig%2C+A">Alexander Wettig</a>, <a href="/search/cs?searchtype=author&query=Heineman%2C+D">David Heineman</a>, <a href="/search/cs?searchtype=author&query=Tafjord%2C+O">Oyvind Tafjord</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Soldaini%2C+L">Luca Soldaini</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+N+A">Noah A. Smith</a>, <a href="/search/cs?searchtype=author&query=Groeneveld%2C+D">Dirk Groeneveld</a>, <a href="/search/cs?searchtype=author&query=Koh%2C+P+W">Pang Wei Koh</a>, <a href="/search/cs?searchtype=author&query=Dodge%2C+J">Jesse Dodge</a>, <a href="/search/cs?searchtype=author&query=Hajishirzi%2C+H">Hannaneh Hajishirzi</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="2412.04403v1-abstract-short" style="display: inline;"> We develop task scaling laws and model ladders to predict the individual task performance of pretrained language models (LMs) in the overtrained setting. Standard power laws for language modeling loss cannot accurately model task performance. Therefore, we leverage a two-step prediction approach: first use model and data size to predict a task-specific loss, and then use this task loss to predict… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.04403v1-abstract-full').style.display = 'inline'; document.getElementById('2412.04403v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.04403v1-abstract-full" style="display: none;"> We develop task scaling laws and model ladders to predict the individual task performance of pretrained language models (LMs) in the overtrained setting. Standard power laws for language modeling loss cannot accurately model task performance. Therefore, we leverage a two-step prediction approach: first use model and data size to predict a task-specific loss, and then use this task loss to predict task performance. We train a set of small-scale "ladder" models, collect data points to fit the parameterized functions of the two prediction steps, and make predictions for two target models: a 7B model trained to 4T tokens and a 13B model trained to 5T tokens. Training the ladder models only costs 1% of the compute used for the target models. On four multiple-choice tasks written in ranked classification format, we can predict the accuracy of both target models within 2 points of absolute error. We have higher prediction error on four other tasks (average absolute error 6.9) and find that these are often tasks with higher variance in task metrics. We also find that using less compute to train fewer ladder models tends to deteriorate predictions. Finally, we empirically show that our design choices and the two-step approach lead to superior performance in establishing scaling laws. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.04403v1-abstract-full').style.display = 'none'; document.getElementById('2412.04403v1-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> 5 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.15661">arXiv:2410.15661</a> <span> [<a href="https://arxiv.org/pdf/2410.15661">pdf</a>, <a href="https://arxiv.org/format/2410.15661">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="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/10.18653/v1/2024.emnlp-main.1176">10.18653/v1/2024.emnlp-main.1176 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Scalable Data Ablation Approximations for Language Models through Modular Training and Merging </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Na%2C+C">Clara Na</a>, <a href="/search/cs?searchtype=author&query=Magnusson%2C+I">Ian Magnusson</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Sherborne%2C+T">Tom Sherborne</a>, <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=Dodge%2C+J">Jesse Dodge</a>, <a href="/search/cs?searchtype=author&query=Dasigi%2C+P">Pradeep Dasigi</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="2410.15661v1-abstract-short" style="display: inline;"> Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance. However, a thorough data ablation study exploring large sets of candidate data mixtures is typically prohibitively expensive since the full effect is seen only after training the models; this can lead practitioners to settle for sub-optimal data mixtures. We propose an efficient metho… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15661v1-abstract-full').style.display = 'inline'; document.getElementById('2410.15661v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.15661v1-abstract-full" style="display: none;"> Training data compositions for Large Language Models (LLMs) can significantly affect their downstream performance. However, a thorough data ablation study exploring large sets of candidate data mixtures is typically prohibitively expensive since the full effect is seen only after training the models; this can lead practitioners to settle for sub-optimal data mixtures. We propose an efficient method for approximating data ablations which trains individual models on subsets of a training corpus and reuses them across evaluations of combinations of subsets. In continued pre-training experiments, we find that, given an arbitrary evaluation set, the perplexity score of a single model trained on a candidate set of data is strongly correlated with perplexity scores of parameter averages of models trained on distinct partitions of that data. From this finding, we posit that researchers and practitioners can conduct inexpensive simulations of data ablations by maintaining a pool of models that were each trained on partitions of a large training corpus, and assessing candidate data mixtures by evaluating parameter averages of combinations of these models. This approach allows for substantial improvements in amortized training efficiency -- scaling only linearly with respect to new data -- by enabling reuse of previous training computation, opening new avenues for improving model performance through rigorous, incremental data assessment and mixing. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.15661v1-abstract-full').style.display = 'none'; document.getElementById('2410.15661v1-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">EMNLP 2024. 17 pages</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.00838">arXiv:2402.00838</a> <span> [<a href="https://arxiv.org/pdf/2402.00838">pdf</a>, <a href="https://arxiv.org/format/2402.00838">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"> OLMo: Accelerating the Science of Language Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Groeneveld%2C+D">Dirk Groeneveld</a>, <a href="/search/cs?searchtype=author&query=Beltagy%2C+I">Iz Beltagy</a>, <a href="/search/cs?searchtype=author&query=Walsh%2C+P">Pete Walsh</a>, <a href="/search/cs?searchtype=author&query=Bhagia%2C+A">Akshita Bhagia</a>, <a href="/search/cs?searchtype=author&query=Kinney%2C+R">Rodney Kinney</a>, <a href="/search/cs?searchtype=author&query=Tafjord%2C+O">Oyvind Tafjord</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Ivison%2C+H">Hamish Ivison</a>, <a href="/search/cs?searchtype=author&query=Magnusson%2C+I">Ian Magnusson</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Y">Yizhong Wang</a>, <a href="/search/cs?searchtype=author&query=Arora%2C+S">Shane Arora</a>, <a href="/search/cs?searchtype=author&query=Atkinson%2C+D">David Atkinson</a>, <a href="/search/cs?searchtype=author&query=Authur%2C+R">Russell Authur</a>, <a href="/search/cs?searchtype=author&query=Chandu%2C+K+R">Khyathi Raghavi Chandu</a>, <a href="/search/cs?searchtype=author&query=Cohan%2C+A">Arman Cohan</a>, <a href="/search/cs?searchtype=author&query=Dumas%2C+J">Jennifer Dumas</a>, <a href="/search/cs?searchtype=author&query=Elazar%2C+Y">Yanai Elazar</a>, <a href="/search/cs?searchtype=author&query=Gu%2C+Y">Yuling Gu</a>, <a href="/search/cs?searchtype=author&query=Hessel%2C+J">Jack Hessel</a>, <a href="/search/cs?searchtype=author&query=Khot%2C+T">Tushar Khot</a>, <a href="/search/cs?searchtype=author&query=Merrill%2C+W">William Merrill</a>, <a href="/search/cs?searchtype=author&query=Morrison%2C+J">Jacob Morrison</a>, <a href="/search/cs?searchtype=author&query=Muennighoff%2C+N">Niklas Muennighoff</a>, <a href="/search/cs?searchtype=author&query=Naik%2C+A">Aakanksha Naik</a>, <a href="/search/cs?searchtype=author&query=Nam%2C+C">Crystal Nam</a> , et al. (18 additional authors not shown) </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="2402.00838v4-abstract-short" style="display: inline;"> Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.00838v4-abstract-full').style.display = 'inline'; document.getElementById('2402.00838v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.00838v4-abstract-full" style="display: none;"> Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the importance of these details in scientifically studying these models, including their biases and potential risks, we believe it is essential for the research community to have access to powerful, truly open LMs. To this end, we have built OLMo, a competitive, truly Open Language Model, to enable the scientific study of language models. Unlike most prior efforts that have only released model weights and inference code, we release OLMo alongside open training data and training and evaluation code. We hope this release will empower the open research community and inspire a new wave of innovation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.00838v4-abstract-full').style.display = 'none'; document.getElementById('2402.00838v4-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 1 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/2402.00159">arXiv:2402.00159</a> <span> [<a href="https://arxiv.org/pdf/2402.00159">pdf</a>, <a href="https://arxiv.org/format/2402.00159">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"> Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Soldaini%2C+L">Luca Soldaini</a>, <a href="/search/cs?searchtype=author&query=Kinney%2C+R">Rodney Kinney</a>, <a href="/search/cs?searchtype=author&query=Bhagia%2C+A">Akshita Bhagia</a>, <a href="/search/cs?searchtype=author&query=Schwenk%2C+D">Dustin Schwenk</a>, <a href="/search/cs?searchtype=author&query=Atkinson%2C+D">David Atkinson</a>, <a href="/search/cs?searchtype=author&query=Authur%2C+R">Russell Authur</a>, <a href="/search/cs?searchtype=author&query=Bogin%2C+B">Ben Bogin</a>, <a href="/search/cs?searchtype=author&query=Chandu%2C+K">Khyathi Chandu</a>, <a href="/search/cs?searchtype=author&query=Dumas%2C+J">Jennifer Dumas</a>, <a href="/search/cs?searchtype=author&query=Elazar%2C+Y">Yanai Elazar</a>, <a href="/search/cs?searchtype=author&query=Hofmann%2C+V">Valentin Hofmann</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+S">Sachin Kumar</a>, <a href="/search/cs?searchtype=author&query=Lucy%2C+L">Li Lucy</a>, <a href="/search/cs?searchtype=author&query=Lyu%2C+X">Xinxi Lyu</a>, <a href="/search/cs?searchtype=author&query=Lambert%2C+N">Nathan Lambert</a>, <a href="/search/cs?searchtype=author&query=Magnusson%2C+I">Ian Magnusson</a>, <a href="/search/cs?searchtype=author&query=Morrison%2C+J">Jacob Morrison</a>, <a href="/search/cs?searchtype=author&query=Muennighoff%2C+N">Niklas Muennighoff</a>, <a href="/search/cs?searchtype=author&query=Naik%2C+A">Aakanksha Naik</a>, <a href="/search/cs?searchtype=author&query=Nam%2C+C">Crystal Nam</a>, <a href="/search/cs?searchtype=author&query=Peters%2C+M+E">Matthew E. Peters</a>, <a href="/search/cs?searchtype=author&query=Ravichander%2C+A">Abhilasha Ravichander</a>, <a href="/search/cs?searchtype=author&query=Richardson%2C+K">Kyle Richardson</a>, <a href="/search/cs?searchtype=author&query=Shen%2C+Z">Zejiang Shen</a> , et al. (11 additional authors not shown) </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="2402.00159v2-abstract-short" style="display: inline;"> Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training dat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.00159v2-abstract-full').style.display = 'inline'; document.getElementById('2402.00159v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.00159v2-abstract-full" style="display: none;"> Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open models are often released without accompanying training data or recipes to reproduce them. As a result, it is challenging to conduct and advance scientific research on language modeling, such as understanding how training data impacts model capabilities and limitations. To facilitate scientific research on language model pretraining, we curate and release Dolma, a three-trillion-token English corpus, built from a diverse mixture of web content, scientific papers, code, public-domain books, social media, and encyclopedic materials. We extensively document Dolma, including its design principles, details about its construction, and a summary of its contents. We present analyses and experimental results on intermediate states of Dolma to share what we have learned about important data curation practices. Finally, we open-source our data curation toolkit to enable reproduction of our work as well as support further research in large-scale data curation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.00159v2-abstract-full').style.display = 'none'; document.getElementById('2402.00159v2-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> 6 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 January, 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">Accepted at ACL 2024; Dataset: https://hf.co/datasets/allenai/dolma; Code: https://github.com/allenai/dolma</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.10523">arXiv:2312.10523</a> <span> [<a href="https://arxiv.org/pdf/2312.10523">pdf</a>, <a href="https://arxiv.org/format/2312.10523">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"> Paloma: A Benchmark for Evaluating Language Model Fit </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Magnusson%2C+I">Ian Magnusson</a>, <a href="/search/cs?searchtype=author&query=Bhagia%2C+A">Akshita Bhagia</a>, <a href="/search/cs?searchtype=author&query=Hofmann%2C+V">Valentin Hofmann</a>, <a href="/search/cs?searchtype=author&query=Soldaini%2C+L">Luca Soldaini</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Tafjord%2C+O">Oyvind Tafjord</a>, <a href="/search/cs?searchtype=author&query=Schwenk%2C+D">Dustin Schwenk</a>, <a href="/search/cs?searchtype=author&query=Walsh%2C+E+P">Evan Pete Walsh</a>, <a href="/search/cs?searchtype=author&query=Elazar%2C+Y">Yanai Elazar</a>, <a href="/search/cs?searchtype=author&query=Lo%2C+K">Kyle Lo</a>, <a href="/search/cs?searchtype=author&query=Groeneveld%2C+D">Dirk Groeneveld</a>, <a href="/search/cs?searchtype=author&query=Beltagy%2C+I">Iz Beltagy</a>, <a href="/search/cs?searchtype=author&query=Hajishirzi%2C+H">Hannaneh Hajishirzi</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+N+A">Noah A. Smith</a>, <a href="/search/cs?searchtype=author&query=Richardson%2C+K">Kyle Richardson</a>, <a href="/search/cs?searchtype=author&query=Dodge%2C+J">Jesse Dodge</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="2312.10523v2-abstract-short" style="display: inline;"> Evaluations of language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains--varying distributions of language. We introduce Perplexity Analysis for Language Model Assessment (Paloma), a benchmark to measure LM fit to 546 English and code domains, instead of assuming perplexity on one distribution extrapolate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10523v2-abstract-full').style.display = 'inline'; document.getElementById('2312.10523v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.10523v2-abstract-full" style="display: none;"> Evaluations of language models (LMs) commonly report perplexity on monolithic data held out from training. Implicitly or explicitly, this data is composed of domains--varying distributions of language. We introduce Perplexity Analysis for Language Model Assessment (Paloma), a benchmark to measure LM fit to 546 English and code domains, instead of assuming perplexity on one distribution extrapolates to others. We include two new datasets of the top 100 subreddits (e.g., r/depression on Reddit) and programming languages (e.g., Java on GitHub), both sources common in contemporary LMs. With our benchmark, we release 6 baseline 1B LMs carefully controlled to provide fair comparisons about which pretraining corpus is best and code for others to apply those controls to their own experiments. Our case studies demonstrate how the fine-grained results from Paloma surface findings such as that models pretrained without data beyond Common Crawl exhibit anomalous gaps in LM fit to many domains or that loss is dominated by the most frequently occurring strings in the vocabulary. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.10523v2-abstract-full').style.display = 'none'; document.getElementById('2312.10523v2-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 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 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">Conference: NeurIPS 2024, Project Page: https://paloma.allen.ai/</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2305.14864">arXiv:2305.14864</a> <span> [<a href="https://arxiv.org/pdf/2305.14864">pdf</a>, <a href="https://arxiv.org/format/2305.14864">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"> Just CHOP: Embarrassingly Simple LLM Compression </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Sherborne%2C+T">Tom Sherborne</a>, <a href="/search/cs?searchtype=author&query=Walsh%2C+E+P">Evan Pete Walsh</a>, <a href="/search/cs?searchtype=author&query=Groeneveld%2C+D">Dirk Groeneveld</a>, <a href="/search/cs?searchtype=author&query=Strubell%2C+E">Emma Strubell</a>, <a href="/search/cs?searchtype=author&query=Beltagy%2C+I">Iz Beltagy</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="2305.14864v3-abstract-short" style="display: inline;"> Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint. A growing assortment of methods for compression promises to reduce the computational burden of LLMs in deployment, but so far, only quantization approaches have been demonstrated to be effective for LLM compression while maintaining zero-shot performance. A critical ste… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.14864v3-abstract-full').style.display = 'inline'; document.getElementById('2305.14864v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2305.14864v3-abstract-full" style="display: none;"> Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint. A growing assortment of methods for compression promises to reduce the computational burden of LLMs in deployment, but so far, only quantization approaches have been demonstrated to be effective for LLM compression while maintaining zero-shot performance. A critical step in the compression process, the pretrain-then-finetune paradigm, has largely been overlooked when adapting existing pruning strategies to LLMs or proposing new ones. In this work, we show that embarrassingly simple layer pruning coupled with an extended language model pretraining as the finetuning phase produces state-of-the-art results against structured and even semi-structured compression of models at a 7B scale while being more inference efficient. We call this method LayerChop, where we deterministically remove layers from a model followed by task-agnostic finetuning of the remaining weights by continued self-supervised pretraining. At this scale, we also show how distillation, which has been super effective in task-agnostic compression of smaller BERT-style models, becomes inefficient against our simple pruning technique. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2305.14864v3-abstract-full').style.display = 'none'; document.getElementById('2305.14864v3-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> 9 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 May, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 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">13 pages, 6 figures, 6 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/2107.12329">arXiv:2107.12329</a> <span> [<a href="https://arxiv.org/pdf/2107.12329">pdf</a>, <a href="https://arxiv.org/format/2107.12329">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"> AASAE: Augmentation-Augmented Stochastic Autoencoders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Falcon%2C+W">William Falcon</a>, <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Koker%2C+T">Teddy Koker</a>, <a href="/search/cs?searchtype=author&query=Cho%2C+K">Kyunghyun Cho</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="2107.12329v2-abstract-short" style="display: inline;"> Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentation pipelines which generate multiple views of a single input that preserve the underlying semantics. In this work, we introduce augmentation-augmented stochastic autoencoders (AASAE), yet another alternative to self-sup… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.12329v2-abstract-full').style.display = 'inline'; document.getElementById('2107.12329v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2107.12329v2-abstract-full" style="display: none;"> Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentation pipelines which generate multiple views of a single input that preserve the underlying semantics. In this work, we introduce augmentation-augmented stochastic autoencoders (AASAE), yet another alternative to self-supervised learning, based on autoencoding. We derive AASAE starting from the conventional variational autoencoder (VAE), by replacing the KL divergence regularization, which is agnostic to the input domain, with data augmentations that explicitly encourage the internal representations to encode domain-specific invariances and equivariances. We empirically evaluate the proposed AASAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated. Our experiments confirm the effectiveness of data augmentation as a replacement for KL divergence regularization. The AASAE outperforms the VAE by 30% on CIFAR-10, 40% on STL-10 and 45% on Imagenet. On CIFAR-10 and STL-10, the results for AASAE are largely comparable to the state-of-the-art algorithms for self-supervised learning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2107.12329v2-abstract-full').style.display = 'none'; document.getElementById('2107.12329v2-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> 6 February, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 26 July, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2021. </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">17 pages, 5 figures, 3 table</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1804.10469">arXiv:1804.10469</a> <span> [<a href="https://arxiv.org/pdf/1804.10469">pdf</a>, <a href="https://arxiv.org/format/1804.10469">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> </div> </div> <p class="title is-5 mathjax"> Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jha%2C+A+H">Ananya Harsh Jha</a>, <a href="/search/cs?searchtype=author&query=Anand%2C+S">Saket Anand</a>, <a href="/search/cs?searchtype=author&query=Singh%2C+M">Maneesh Singh</a>, <a href="/search/cs?searchtype=author&query=Veeravasarapu%2C+V+S+R">V. S. R. Veeravasarapu</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="1804.10469v1-abstract-short" style="display: inline;"> Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation ar… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.10469v1-abstract-full').style.display = 'inline'; document.getElementById('1804.10469v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1804.10469v1-abstract-full" style="display: none;"> Generative models that learn disentangled representations for different factors of variation in an image can be very useful for targeted data augmentation. By sampling from the disentangled latent subspace of interest, we can efficiently generate new data necessary for a particular task. Learning disentangled representations is a challenging problem, especially when certain factors of variation are difficult to label. In this paper, we introduce a novel architecture that disentangles the latent space into two complementary subspaces by using only weak supervision in form of pairwise similarity labels. Inspired by the recent success of cycle-consistent adversarial architectures, we use cycle-consistency in a variational auto-encoder framework. Our non-adversarial approach is in contrast with the recent works that combine adversarial training with auto-encoders to disentangle representations. We show compelling results of disentangled latent subspaces on three datasets and compare with recent works that leverage adversarial training. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1804.10469v1-abstract-full').style.display = 'none'; document.getElementById('1804.10469v1-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> 27 April, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2018. </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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