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cs.CL2025
MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels
Xiaoou Liu, Zhen Lin, Longchao Da +3
Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs can lead to significant consequ…
cs.CL2024
Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation
Zhen Lin, Shubhendu Trivedi, Jimeng Sun
The advent of large language models (LLMs) has dramatically advanced the state-of-the-art in numerous natural language generation tasks. For LLMs to be applied reliably, it is esse…
cs.CL2024
Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models
Zhen Lin, Shubhendu Trivedi, Jimeng Sun
Large language models (LLMs) specializing in natural language generation (NLG) have recently started exhibiting promising capabilities across a variety of domains. However, gauging…