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Large Language Models (LLMs) are expected to be predictable and trustworthy to support reliable decision-making systems.
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Bias and Fairness in Large Language Models: A Survey
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A logic-driven framework for consistency of neural models
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Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
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Palm: Scaling language modeling with pathways, 2022
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BECEL: Benchmark for consistency evaluation of language models
Jang, M., Kwon, D. S., and Lukasiewicz, T · 2022
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Maieutic prompting: Logically consistent reasoning with recursive explanations
Jung, J., Qin, L., Welleck, S., Brahman, F., Bhagavatula, C., Bras, R. L., and Choi, Y · 2022
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Zephyr: Direct distillation of lm alignment, 2023
Tunstall, L., Beeching, E., Lambert, N., Rajani, N., Rasul, K., Belkada, Y., Huang, S., von Werra, L., Fourrier, C., Habib, N., Sarrazin, N., Sanseviero, O., Rush, A. M., and Wolf, T · 2023
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Siren’s song in the AI ocean: A survey on hallucination in large language models
Zhang, Y., Li, Y., Cui, L., Cai, D., Liu, L., Fu, T., Huang, X., Zhao, E., Zhang, Y., Chen, Y., Wang, L., Luu, A. T., Bi, W., Shi, F., and Shi, S · 2023
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Large language models are not robust multiple choice selectors
Zheng, C., Zhou, H., Meng, F., Zhou, J., and Huang, M · 2023
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Phi-3 technical report: A highly capable language model locally on your phone
Abdin, M. I., Jacobs, S. A., Awan, A. A., et al · 2024
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The reversal curse: Llms trained on ”a is b” fail to learn ”b is a”
Berglund, L., Tong, M., Kaufmann, M., Balesni, M., Stickland, A. C., Korbak, T., and Evans, O · 2024
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Safe RLHF: Safe reinforcement learning from human feedback
Dai, J., Pan, X., Sun, R., Ji, J., Xu, X., Liu, M., Wang, Y., and Yang, Y · 2024
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Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model
DeepSeek-AI · 2024
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Dubey, A., Jauhri, A., Pandey, A., et al · 2024
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Guo, Z., Liu, M., Ji, Z., Bai, J., Guo, Y., and Zuo, W · 2024
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Aligning with human judgement: The role of pairwise preference in large language model evaluators
Liu, Y., Zhou, H., Guo, Z., Shareghi, E., Vulić, I., Korhonen, A., and Collier, N · 2024
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Teacher-student training for debiasing: General permutation debiasing for large language models
Liusie, A., Fathullah, Y., and Gales, M. J. F · 2024
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Large language models are effective text rankers with pairwise ranking prompting
Qin, Z., Jagerman, R., Hui, K., Zhuang, H., Wu, J., Yan, L., Shen, J., Liu, T., Liu, J., Metzler, D., Wang, X., and Bendersky, M · 2024
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Gemma 2: Improving open language models at a practical size
Rivière, M., Pathak, S., Sessa, P. G., et al · 2024
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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 · 2024
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Large language models are not fair evaluators
Wang, P., Li, L., Chen, L., Cai, Z., Zhu, D., Lin, B., Cao, Y., Kong, L., Liu, Q., Liu, T., and Sui, Z · 2024
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Large language models as optimizers
Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X · 2024
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Fairer preferences elicit improved human-aligned large language model judgments, 2024
Zhou, H., Wan, X., Liu, Y., Collier, N., Vulić, I., and Korhonen, A · 2024
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