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Despite their sophisticated capabilities, large language models (LLMs) encounter a major hurdle in effective assessment.
A holistic framework to improve the uptake and impact of ehealth technologies
van Gemert-Pijnen, J. E., Nijland, N., van Limburg, M., Ossebaard, H. C., Kelders, S. M., Eysenbach, G., and Seydel, E. R · 2011
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Findings of the 2014 workshop on statistical machine translation
Bojar, O., Buck, C., Federmann, C., Haddow, B., Koehn, P., Leveling, J., Monz, C., Pecina, P., Post, M., Saint-Amand, H., et al · 2014
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., and Fidler, S · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Findings of the 2016 conference on machine translation (wmt16)
Bojar, O., Chatterjee, R., Federmann, C., Graham, Y., Haddow, B., Huck, M., Yepes, A. J., Koehn, P., Logacheva, V., Monz, C., et al · 2016
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Opensubtitles2016: Extracting large parallel corpora from movie and tv subtitles
Lison, P. and Tiedemann, J · 2016
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Race: Large-scale reading comprehension dataset from examinations
Lai, G., Xie, Q., Liu, H., Yang, Y., and Hovy, E · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., and Tafjord, O · 2018
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Hierarchical neural story generation
Fan, A., Lewis, M., and Dauphin, Y · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Mihaylov, T., Clark, P., Khot, T., and Sabharwal, A · 2018
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Improving language understanding by generative pre-training
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al · 2018
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Commonsenseqa: A question answering challenge targeting commonsense knowledge
Talmor, A., Herzig, J., Lourie, N., and Berant, J · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., and Toutanova, K · 2019
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The curious case of neural text degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., and Choi, Y · 2019
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, D., Chen, M., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., et al · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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Socialiqa: Commonsense reasoning about social interactions
Sap, M., Rashkin, H., Chen, D., LeBras, R., and Choi, Y · 2019
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Megatron-lm: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., and Catanzaro, B · 2019
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Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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Piqa: Reasoning about physical commonsense in natural language
Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al · 2020
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2020
Cited alongside, same era.
Qasc: A dataset for question answering via sentence composition
Khot, T., Clark, P., Guerquin, M., Jansen, P., and Sabharwal, A · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Cited alongside, same era.
Zero: Memory optimizations toward training trillion parameter models
Rajbhandari, S., Rasley, J., Ruwase, O., and He, Y · 2020
Cited alongside, same era.
Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Rasley, J., Rajbhandari, S., Ruwase, O., and He, Y · 2020
Cited alongside, same era.
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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The falcon series of open language models
Almazrouei, E., Alobeidli, H., Alshamsi, A., Cappelli, A., Cojocaru, R., Debbah, M., Goffinet, É., Hesslow, D., Launay, J., Malartic, Q., et al · 2023
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
bench authors, B · 2023
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Accelerating large language model decoding with speculative sampling
Chen, C., Borgeaud, S., Irving, G., Lespiau, J.-B., Sifre, L., and Jumper, J · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality
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Shazeer, N · 2020
Cited alongside, same era.
Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C., Terry, M., Le, Q., et al · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
Cited alongside, same era.
Training verifiers to solve math word problems
Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., and Schulman, J · 2021
Cited alongside, same era.
A framework for few-shot language model evaluation
Gao, L., Tow, J., Biderman, S., Black, S., DiPofi, A., Foster, C., Golding, L., Hsu, J., McDonell, K., Muennighoff, N., et al · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2021
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Rae, J. W., Borgeaud, S., Cai, T., Millican, K., Hoffmann, J., Song, F., Aslanides, J., Henderson, S., Ring, R., Young, S., et al · 2021
Cited alongside, same era.
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., et al · 2023
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Palm: Scaling language modeling with pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al · 2023
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Opencompass: A universal evaluation platform for foundation models
Contributors, O · 2023
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Longnet: Scaling transformers to 1,000,000,000 tokens
Ding, J., Ma, S., Dong, L., Zhang, X., Huang, S., Wang, W., Zheng, N., and Wei, F · 2023
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C-eval: A multi-level multi-discipline chinese evaluation suite for foundation models
Huang, Y., Bai, Y., Zhu, Z., Zhang, J., Zhang, J., Su, T., Liu, J., Lv, C., Zhang, Y., Lei, J., Fu, Y., Sun, M., and He, J · 2023
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Fast inference from transformers via speculative decoding
Leviathan, Y., Kalman, M., and Matias, Y · 2023
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Rwkv: Reinventing rnns for the transformer era
Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Cao, H., Cheng, X., Chung, M., Grella, M., GV, K. K., et al · 2023
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Retentive network: A successor to transformer for large language models
Sun, Y., Dong, L., Huang, S., Ma, S., Xia, Y., Xue, J., Wang, J., and Wei, F · 2023
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Alpaca: A strong, replicable instruction-following model
Taori, R., Gulrajani, I., Zhang, T., Dubois, Y., Li, X., Guestrin, C., Liang, P., and Hashimoto, T. B · 2023
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Xwin-lm, 9 2023
Team, X.-L · 2023
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Xwin-lm, 9 2023
Team, X.-L · 2023
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Wizardlm: Empowering large language models to follow complex instructions
Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., and Jiang, D · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E., et al · 2023
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Agieval: A human-centric benchmark for evaluating foundation models
Zhong, W., Cui, R., Guo, Y., Liang, Y., Lu, S., Wang, Y., Saied, A., Chen, W., and Duan, N · 2023
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Anytool: Self-reflective, hierarchical agents for large-scale api calls
Du, Y., Wei, F., and Zhang, H · 2024
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Eagle: Speculative sampling requires rethinking feature uncertainty
Li, Y., Wei, F., Zhang, C., and Zhang, H · 2024
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Roformer: Enhanced transformer with rotary position embedding
Su, J., Ahmed, M., Lu, Y., Pan, S., Bo, W., and Liu, Y · 2024
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