Constructing Your Model’s Value Distinction: Towards LLM Alignment with Anchor Words Tuning (2025.findings-emnlp)
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| Challenge: | a study of large language models (LLMs) shows that they can generate outputs that are honest, positive, harmless, etc. |
| Approach: | They propose a method that amplifies logits difference between positive and negative tokens . they propose to use the logits gap to generate positive and positive tokens after alignment . |
| Outcome: | The proposed method achieves effective alignment, but requires fewer computational resources compared to training-time alignment methods. |
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| Challenge: | Existing methods for generating large language models have been criticized for their complexity and instability. |
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| Challenge: | Large Language Models (LLMs) show remarkable performance across tasks . alignment with human values is critical for their responsible development. |
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| Challenge: | Typically, the human feedback is used to train a proxy reward model (RM), and a policy model is optimized over the reward signal from the RM using RL. |
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Do LLMs Align Human Values Regarding Social Biases? Judging and Explaining Social Biases with LLMs (2025.findings-emnlp)
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| Challenge: | Large language models can lead to undesired consequences when misaligned with human values . previous studies have shown misalignment of LLMs with human value using expert-designed or agent-based emulated bias scenarios . |
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| Challenge: | Existing methods to reduce LLMs' biased outputs rely on reward signals from current model outputs without considering the source of biases. |
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Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization (2024.findings-emnlp)
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| Challenge: | Existing methods for training large language models require additional annotations to adjust to shifted distributions. |
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Aligning Large Language Models with Human Preferences through Representation Engineering (2024.acl-long)
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Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing methods for achieving this alignment involve employing reinforcement learning from human feedback (RLHF) Existing approaches involve using RLHF to fine-tune LLMs based on human labels . however, RLRF is susceptible to instability during fine- tuning and presents challenges in implementation. |
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