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2,206.02336 | 2,206.02336 | Making Large Language Models Better Reasoners with Step-Aware Verifier | Few-shot learning is a challenging task that requires language models to
generalize from limited examples. Large language models like GPT-3 and PaLM
have made impressive progress in this area, but they still face difficulties in
reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve
their reason... | http://arxiv.org/pdf/2206.02336 | ['Yifei Li' 'Zeqi Lin' 'Shizhuo Zhang' 'Qiang Fu' 'Bei Chen'
'Jian-Guang Lou' 'Weizhu Chen'] | ['cs.CL' 'cs.AI'] | null | null | cs.CL | 20,220,606 | 20,230,524 |
* D. Andor, L. He, K. Lee, and E. Pitler (2019)Giving BERT a calculator: finding operations and arguments with reading comprehension. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong... | # Making Large Language Models Better Reasoners with Step-Aware Verifier
Yifei Li\({}^{1,2}\); Zeqi Lin\({}^{2}\), Shizhuo Zhang\({}^{2}\), Qiang Fu\({}^{2}\), Bei Chen\({}^{2}\),
Jian-Guang Lou\({}^{2}\), Weizhu Chen\({}^{2}\)
\({}^{1}\) National Key Laboratory for Multimedia Information Processing, School of Compu... | # Making Large Language Models Better Reasoners with Step-Aware Verifier
Yifei Li\({}^{1,2}\); Zeqi Lin\({}^{2}\), Shizhuo Zhang\({}^{2}\), Qiang Fu\({}^{2}\), Bei Chen\({}^{2}\),
Jian-Guang Lou\({}^{2}\), Weizhu Chen\({}^{2}\)
\({}^{1}\) National Key Laboratory for Multimedia Information Processing, School of Compu... |
2,206.04615 | 2,206.04615 | Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models | "Language models demonstrate both quantitative improvement and new qualitative\ncapabilities with in(...TRUNCATED) | http://arxiv.org/pdf/2206.04615 | "['Aarohi Srivastava' 'Abhinav Rastogi' 'Abhishek Rao' 'Abu Awal Md Shoeb'\n 'Abubakar Abid' 'Adam F(...TRUNCATED) | ['cs.CL' 'cs.AI' 'cs.CY' 'cs.LG' 'stat.ML'] | 27 pages, 17 figures + references and appendices, repo:
https://github.com/google/BIG-bench | Transactions on Machine Learning Research, May/2022,
https://openreview.net/forum?id=uyTL5Bvosj | cs.CL | 20,220,609 | 20,230,612 | "\n\n* Wikiquote et al. (2021) Wikiquote, russian proverbs. URL [https://ru.wikiquote.org/wiki/%DO%A(...TRUNCATED) | "# Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models\n\nA(...TRUNCATED) | "# Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models\n\nA(...TRUNCATED) |
2,206.05229 | 2,206.05229 | Measuring the Carbon Intensity of AI in Cloud Instances | "By providing unprecedented access to computational resources, cloud computing\nhas enabled rapid gr(...TRUNCATED) | http://arxiv.org/pdf/2206.05229 | "['Jesse Dodge' 'Taylor Prewitt' 'Remi Tachet Des Combes' 'Erika Odmark'\n 'Roy Schwartz' 'Emma Stru(...TRUNCATED) | ['cs.LG'] | In ACM Conference on Fairness, Accountability, and Transparency (ACM
FAccT) 2022 | null | cs.LG | 20,220,610 | 20,220,610 | "\n\n* (1)\n* Anthony et al. (2020) Lasse F. Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan(...TRUNCATED) | "[MISSING_PAGE_EMPTY:1]\n\nIntroduction\n\nClimate change is an increasing threat to life on our pla(...TRUNCATED) | "[MISSING_PAGE_EMPTY:1]\n\nIntroduction\n\nClimate change is an increasing threat to life on our pla(...TRUNCATED) |
2,206.05802 | 2,206.05802 | Self-critiquing models for assisting human evaluators | "We fine-tune large language models to write natural language critiques\n(natural language critical (...TRUNCATED) | http://arxiv.org/pdf/2206.05802 | "['William Saunders' 'Catherine Yeh' 'Jeff Wu' 'Steven Bills' 'Long Ouyang'\n 'Jonathan Ward' 'Jan L(...TRUNCATED) | ['cs.CL' 'cs.LG'] | null | null | cs.CL | 20,220,612 | 20,220,614 | " (RLHP) has become more common [1, 2, 3, 4], demonstrating empirically a technique that lets us rea(...TRUNCATED) | "# Self-critiquing models for assisting human evaluators\n\nWilliam Saunders\n\n&Catherine Yeh\n\n&J(...TRUNCATED) | "# Self-critiquing models for assisting human evaluators\n\nWilliam Saunders\n\n&Catherine Yeh\n\n&J(...TRUNCATED) |
2,206.06336 | 2,206.06336 | Language Models are General-Purpose Interfaces | "Foundation models have received much attention due to their effectiveness\nacross a broad range of (...TRUNCATED) | http://arxiv.org/pdf/2206.06336 | "['Yaru Hao' 'Haoyu Song' 'Li Dong' 'Shaohan Huang' 'Zewen Chi'\n 'Wenhui Wang' 'Shuming Ma' 'Furu W(...TRUNCATED) | ['cs.CL'] | 32 pages. The first three authors contribute equally | null | cs.CL | 20,220,613 | 20,220,613 | "\n\n* Agrawal et al. (2019) Harsh Agrawal, Karan Desai, Yufei Wang, Xinlei Chen, Rishabh Jain, Mark(...TRUNCATED) | "# Language Models are General-Purpose Interfaces\n\n Yaru Hao, Haoyu Song, Li Dong\n\nShaohan Huang(...TRUNCATED) | "# Language Models are General-Purpose Interfaces\n\n Yaru Hao, Haoyu Song, Li Dong\n\nShaohan Huang(...TRUNCATED) |
2,206.07635 | 2,206.07635 | AI Ethics Issues in Real World: Evidence from AI Incident Database | "With the powerful performance of Artificial Intelligence (AI) also comes\nprevalent ethical issues.(...TRUNCATED) | http://arxiv.org/pdf/2206.07635 | ['Mengyi Wei' 'Zhixuan Zhou'] | ['cs.AI' 'cs.CY'] | 56th Hawaii International Conference on System Sciences (HICSS) | null | cs.AI | 20,220,615 | 20,220,818 | "\n\n* [1] I. Glenn Cohen and Michelle M. Mello. Big data, big tech, and protecting patient privacy.(...TRUNCATED) | "# AI Ethics Issues in Real World: Evidence from AI Incident Database\n\n Mengyi Wei\n\nTechnical Un(...TRUNCATED) | "# AI Ethics Issues in Real World: Evidence from AI Incident Database\n\n Mengyi Wei\n\nTechnical Un(...TRUNCATED) |
2,206.14858 | 2,206.14858 | Solving Quantitative Reasoning Problems with Language Models | "Language models have achieved remarkable performance on a wide range of tasks\nthat require natural(...TRUNCATED) | http://arxiv.org/pdf/2206.14858 | "['Aitor Lewkowycz' 'Anders Andreassen' 'David Dohan' 'Ethan Dyer'\n 'Henryk Michalewski' 'Vinay Ram(...TRUNCATED) | ['cs.CL' 'cs.AI' 'cs.LG'] | 12 pages, 5 figures + references and appendices | null | cs.CL | 20,220,629 | 20,220,701 | "**: A human should be able to solve each problem and understand the given solution without having t(...TRUNCATED) | "# Solving Quantitative Reasoning Problems with Language Models\n\nAitor Lewkowycz, Anders Andreasse(...TRUNCATED) | "# Solving Quantitative Reasoning Problems with Language Models\n\nAitor Lewkowycz, Anders Andreasse(...TRUNCATED) |
2,207.0056 | 2,207.0056 | Is neural language acquisition similar to natural? A chronological probing study | "The probing methodology allows one to obtain a partial representation of\nlinguistic phenomena stor(...TRUNCATED) | http://arxiv.org/pdf/2207.00560 | ['Ekaterina Voloshina' 'Oleg Serikov' 'Tatiana Shavrina'] | ['cs.CL'] | "Published in proceedings of Dialogue-2022 \"Computational Linguistics\n and Intellectual Technolog(...TRUNCATED) | null | cs.CL | 20,220,701 | 20,220,701 | "\n\n* [Belinkov et al.2017] Yonatan Belinkov, Lluis Marquez, Hassan Sajjad, Nadir Durrani, Fahim Da(...TRUNCATED) | "[MISSING_PAGE_FAIL:1]\n\nIntroduction\n\nThe role of deep learning language models has been increas(...TRUNCATED) | "[MISSING_PAGE_FAIL:1]\n\nIntroduction\n\nThe role of deep learning language models has been increas(...TRUNCATED) |
2,207.04672 | 2,207.04672 | No Language Left Behind: Scaling Human-Centered Machine Translation | "Driven by the goal of eradicating language barriers on a global scale,\nmachine translation has sol(...TRUNCATED) | http://arxiv.org/pdf/2207.04672 | "['NLLB Team' 'Marta R. Costa-jussà' 'James Cross' 'Onur Çelebi'\n 'Maha Elbayad' 'Kenneth Heafiel(...TRUNCATED) | ['cs.CL' 'cs.AI' '68T50' 'I.2.7'] | 190 pages | null | cs.CL | 20,220,711 | 20,220,825 | " to evaluation and training data.\n* Primary intended users: _Primary users are researchers and mac(...TRUNCATED) | "**No Language Left Behind:**\n\n**Scaling Human-Centered Machine Translation**\n\nNLLB Team, Marta (...TRUNCATED) | "**No Language Left Behind:**\n\n**Scaling Human-Centered Machine Translation**\n\nNLLB Team, Marta (...TRUNCATED) |
2,207.05221 | 2,207.05221 | Language Models (Mostly) Know What They Know | "We study whether language models can evaluate the validity of their own\nclaims and predict which q(...TRUNCATED) | http://arxiv.org/pdf/2207.05221 | "['Saurav Kadavath' 'Tom Conerly' 'Amanda Askell' 'Tom Henighan'\n 'Dawn Drain' 'Ethan Perez' 'Nicho(...TRUNCATED) | ['cs.CL' 'cs.AI' 'cs.LG'] | 23+17 pages; refs added, typos fixed | null | cs.CL | 20,220,711 | 20,221,121 | "\n\n* [Ahn et al., 2022] Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn,(...TRUNCATED) | "# Language Models (Mostly) Know What They Know\n\n Saurav Kadavath, Tom Conerly, Amanda Askell, Tom(...TRUNCATED) | "# Language Models (Mostly) Know What They Know\n\n Saurav Kadavath, Tom Conerly, Amanda Askell, Tom(...TRUNCATED) |
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Dataset Description
The "arxiv_small_nougat" dataset is a collection of 108 recent papers sourced from arXiv, focusing on topics related to Large Language Models (LLM) and Transformers. These papers have been meticulously processed and parsed using Meta's Nougat model, which is specifically designed to retain the integrity of complex elements such as tables and mathematical equations.
Data Format
The dataset contains the parsed content of the selected papers, with special attention given to the preservation of formatting, tables, and mathematical expressions. Each paper is provided as plain text.
Usage
Researchers, academics, and natural language processing practitioners can leverage this dataset for various tasks related to LLM and Transformers, including:
- Language modeling
- Text summarization
- Information retrieval
- Table and equation extraction
Acknowledgments
We acknowledge the arXiv platform for providing open access to a wealth of research papers in the field of machine learning and natural language processing.
License
[mit]
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