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Truthfulness is paramount for large language models (LLMs) as they are increasingly deployed in real-world applications.
Crafting papers on machine learning
Langley, P · 2000
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 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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Hellaswag: Can a machine really finish your sentence?
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., and Choi, Y · 2019
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Reformulating unsupervised style transfer as paraphrase generation
Krishna, K., Wieting, J., and Iyyer, M · 2020
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Trl: Transformer reinforcement learning
von Werra, L., Belkada, Y., Tunstall, L., Beeching, E., Thrush, T., Lambert, N., and Huang, S · 2020
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A framework for few-shot language model evaluation
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
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Constitutional ai: Harmlessness from ai feedback
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., McKinnon, C., et al · 2022
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Bommarito II, M. and Katz, D. M · 2022
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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 · 2022
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Truthfulqa: Measuring how models mimic human falsehoods
Lin, S., Hilton, J., and Evans, O · 2022
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Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., and Lowe, R · 2022
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A general theoretical paradigm to understand learning from human preferences
Azar, M. G., Rowland, M., Piot, B., Guo, D., Calandriello, D., Valko, M., and Munos, R · 2023
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The internal state of an LLM knows when it’s lying
Azaria, A. and Mitchell, T. M · 2023
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Bang, Y., Cahyawijaya, S., Lee, N., Dai, W., Su, D., Wilie, B., Lovenia, H., Ji, Z., Yu, T., Chung, W., et al · 2023
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Open llm leaderboard
Beeching, E., Fourrier, C., Habib, N., Han, S., Lambert, N., Rajani, N., Sanseviero, O., Tunstall, L., and Wolf, T · 2023
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Discovering latent knowledge in language models without supervision
Burns, C., Ye, H., Klein, D., and Steinhardt, J · 2023
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Poisoning web-scale training datasets is practical
Carlini, N., Jagielski, M., Choquette-Choo, C. A., Paleka, D., Pearce, W., Anderson, H., Terzis, A., Thomas, K., and Tramèr, F · 2023
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What is the impact of chatgpt on education? a rapid review of the literature
Lo, C. K · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., and Finn, C · 2023
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Beyond human data: Scaling self-training for problem-solving with language models
Singh, A., Co-Reyes, J. D., Agarwal, R., Anand, A., Patil, P., Liu, P. J., Harrison, J., Lee, J., Xu, K., Parisi, A., et al · 2023
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Preference ranking optimization for human alignment
Song, F., Yu, B., Li, M., Yu, H., Huang, F., Li, Y., and Wang, H · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., et al · 2023
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, March 2023
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J. E., Stoica, I., and Xing, E. P · 2023
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Dola: Decoding by contrasting layers improves factuality in large language models
Chuang, Y.-S., Xie, Y., Luo, H., Kim, Y., Glass, J., and He, P · 2023
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Qlora: Efficient finetuning of quantized llms
Dettmers, T., Pagnoni, A., Holtzman, A., and Zettlemoyer, L · 2023
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Raft: Reward ranked finetuning for generative foundation model alignment
Dong, H., Xiong, W., Goyal, D., Pan, R., Diao, S., Zhang, J., Shum, K., and Zhang, T · 2023
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Palm-e: An embodied multimodal language model
Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., Huang, W., Chebotar, Y., Sermanet, P., Duckworth, D., Levine, S., Vanhoucke, V., Hausman, K., Toussaint, M., Greff, K., Zeng, A., Mordatch, I., and Florence, P · 2023
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Zyn: Zero-shot reward models with yes-no questions
Gallego, V · 2023
Cited alongside, same era.
Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., Casas, D. d. l., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., et al · 2023
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Zephyr: Direct distillation of lm alignment
Tunstall, L., Beeching, E., Lambert, N., Rajani, N., Rasul, K., Belkada, Y., Huang, S., von Werra, L., Fourrier, C., Habib, N., et al · 2023
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Chatcad: Interactive computer-aided diagnosis on medical image using large language models
Wang, S., Zhao, Z., Ouyang, X., Wang, Q., and Shen, D · 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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Rrhf: Rank responses to align language models with human feedback without tears
Yuan, Z., Yuan, H., Tan, C., Wang, W., Huang, S., and Huang, F · 2023
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How language model hallucinations can snowball
Zhang, M., Press, O., Merrill, W., Liu, A., and Smith, N. A · 2023
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Representation engineering: A top-down approach to ai transparency
Zou, A., Phan, L., Chen, S., Campbell, J., Guo, P., Ren, R., Pan, A., Yin, X., Mazeika, M., Dombrowski, A.-K., et al · 2023
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Self-play fine-tuning converts weak language models to strong language models
Chen, Z., Deng, Y., Yuan, H., Ji, K., and Gu, Q · 2024
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Self-rewarding language models
Yuan, W., Pang, R. Y., Cho, K., Sukhbaatar, S., Xu, J., and Weston, J · 2024
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