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LLMs flip answers with irrelevant context despite stable accuracy scores · 2 sources tracked

New research from Georgia Tech and Stanford University reveals that large language models can exhibit instability in their predictions. While overall accuracy scores may remain high, individual answers can change significantly when the models are presented with irrelevant or meaningless context. This suggests a potential hidden fragility in LLM performance that is not captured by standard evaluation metrics. AI

IMPACT Highlights potential hidden instabilities in LLM outputs, suggesting current evaluation methods may not fully capture model robustness.

RANK_REASON The cluster contains research papers from academic institutions.

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs flip answers with irrelevant context despite stable accuracy scores · 2 sources tracked

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COVERAGE [2]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    LLM accuracy hides prediction flips from irrelevant context arXiv research from Georgia Tech and Stanford shows language models flip individual answers when fed

    LLM accuracy hides prediction flips from irrelevant context arXiv research from Georgia Tech and Stanford shows language models flip individual answers when fed meaningless text, as overall scores hold steady. https://www. notatechguy.com/llm-accuracy-h ides-prediction-flips-from…

  2. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

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