CRITIC improves LLM outputs on question answering, math synthesis, and toxicity reduction by having the model interact with tools to critique and revise its initial generations.
Navigating the grey area: Expressions of overconfidence and uncertainty in language models
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A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.
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CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
CRITIC improves LLM outputs on question answering, math synthesis, and toxicity reduction by having the model interact with tools to critique and revise its initial generations.
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Calibrating Model-Based Evaluation Metrics for Summarization
A reference-free proxy scoring framework combined with GIRB calibration produces better-aligned evaluation metrics for summarization and outperforms baselines across seven datasets.
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Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.