Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)
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| Challenge: | Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs . |
| Approach: | They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique. |
| Outcome: | The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias. |
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| Challenge: | Existing approaches to tackling length bias are limited by their complexity or lack of a linear length-reward relation. |
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Beyond Excess and Deficiency: Adaptive Length Bias Mitigation in Reward Models for RLHF (2025.findings-naacl)
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| Challenge: | Existing efforts to mitigate length bias in reward models have decreased accuracy . achieving an automatic proxy that perfectly replicates human judgment is challenging in practice. |
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) exploits biases in human preferences, such as verbosity, and is under-explored for Direct Alignment Algorithms such as DPO. |
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| Challenge: | Existing work has demonstrated the ability of large language models to learn lexical and label biases in-context negatively impacts performance and robustness of models. |
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| Challenge: | Existing instruction following models fail to follow length constraints in their evaluations. |
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) has shown promise for enhancing pre-trained large language models to generate responses that align with human preferences and societal values. |
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| Challenge: | Large language models (LLMs) often produce unnecessarily long explanations that reduce efficiency. |
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| Challenge: | Existing studies attributed verbosity to biased labels, but new research shows that DPO can be effective in mitigating verboses. |
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Muhan Lin, Shuyang Shi, Yue Guo, Behdad Chalaki, Vaishnav Tadiparthi, Ehsan Moradi Pari, Simon Stepputtis, Joseph Campbell, Katia Sycara
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Debiasing Large Language Models with Structured Knowledge (2024.findings-acl)
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| Challenge: | Existing methods to reduce biases in pre-training models are hampered by their performance. |
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