LGMT is a logic-grounded metamorphic testing framework that detects hidden reasoning defects in LLMs by checking consistency on semantically invariant inputs derived from FOL equivalences.
arXiv preprint arXiv:2002.04326 , year=
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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.
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.
A competition entry achieved efficient fine-tuning of LLaMa2 70B on one GPU in 24 hours with competitive QA benchmark performance.
citing papers explorer
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LGMT: Logic-Grounded Metamorphic Testing for Evaluating the Reasoning Reliability of LLMs
LGMT is a logic-grounded metamorphic testing framework that detects hidden reasoning defects in LLMs by checking consistency on semantically invariant inputs derived from FOL equivalences.
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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.