LLM explanations create a persuasion paradox by increasing confidence without accuracy gains in visual tasks while aiding logical tasks, with uncertainty displays and selective automation outperforming explanations in some cases.
arXiv preprint arXiv:2002.04326 , year=
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The Persuasion Paradox: When LLM Explanations Fail to Improve Human-AI Team Performance
LLM explanations create a persuasion paradox by increasing confidence without accuracy gains in visual tasks while aiding logical tasks, with uncertainty displays and selective automation outperforming explanations in some cases.
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Measuring AI Reasoning: A Guide for Researchers
Reasoning in language models should be measured by the faithfulness and validity of their multi-step search processes and intermediate traces, not final-answer accuracy.
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The nextAI Solution to the NeurIPS 2023 LLM Efficiency Challenge
A competition entry achieved efficient fine-tuning of LLaMa2 70B on one GPU in 24 hours with competitive QA benchmark performance.