From Distributional to Overton Pluralism: Investigating Large Language Model Alignment (2025.naacl-long)
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| Challenge: | a large language model's (LLM) output distribution is changed by an alignment process . a recent study shows that aligned models surface information that cannot be recovered from base models without fine-tuning. |
| Approach: | They analyze two aspects of the alignment process that change output distributions . they find alignment suppresses irrelevant and unhelpful content . |
| Outcome: | The proposed model can be imitated without fine-tuning by using in-context examples and lower-resolution semantic hints about response content. |
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
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| Challenge: | popular training paradigms for language models often assume there is one optimal answer for every query. |
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| Challenge: | Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. |
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| Challenge: | Existing studies suggest large language models can capture certain behavioral patterns, but there are ongoing debates as to whether they are valid replacements for human subjects. |
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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
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