One fish, two fish, but not the whole sea: Alignment reduces language models’ conceptual diversity

2024/11/07 by Sonia K. Murthy, Tomer Ullman, Murthy, Sonia K. +3 · 3 voices · 18 citations
Computer Science · #Natural Language Processing Techniques #Topic Modeling

paper · pdf · doi:10.18653/v1/2025.naacl-long.561

Abstract

Sonia Krishna Murthy, Tomer Ullman, Jennifer Hu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

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Discussions

• (1/9) Excited to share my recent work on "Alignment reduces LM's conceptual diversity" with @tomerullman.bsky.social and @jennhu.bsky.social, to appear at #NAACL2025! 🐟 We want models that match our [bsky, 63 points, 2 comments]
• arxiv.org/abs/2411.04427 [bsky, 2 points, 0 comments]
• Except it turns out that LLMs don't capture the diversity of actual humans, and tuning the model doesn't help. Which also explains why "creative" work by LLMs all has the same boring sameness. They r [bsky, 0 points, 0 comments]

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