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cs.LG2024
Improving LLM Group Fairness on Tabular Data via In-Context Learning
Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar +4
Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instr…
stat.ML2024
Multicalibration for Confidence Scoring in LLMs
Gianluca Detommaso, Martin Bertran, Riccardo Fogliato +1
This paper proposes the use of "multicalibration" to yield interpretable and reliable confidence scores for outputs generated by large language models (LLMs). Multicalibration asks…