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Found 6 papers, 3 papers with code
In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation
Large language models (LLMs) frequently hallucinate and produce factual errors, yet our understanding of why they make these errors remains limited.
FELM: Benchmarking Factuality Evaluation of Large Language Models
In this benchmark, we collect responses generated from LLMs and annotate factuality labels in a fine-grained manner.
Evaluating Factual Consistency of Summaries with Large Language Models
Detecting factual errors in summaries has been an important and challenging subject in summarization research.
Asymptotic Optimality of Myopic Ranking and Selection Procedures
In this paper, we theoretically analyze these myopic procedures and prove that they also satisfy the optimality conditions of R&S, just like some other popular R&S methods.
Convergence Rate Analysis for Optimal Computing Budget Allocation Algorithms
It builds the optimality conditions for the number of samples allocated to each design, and the sample allocation that satisfies the optimality conditions is shown to asymptotically maximize the probability of correct selection for the best design.
On the Finite-Time Performance of the Knowledge Gradient Algorithm
Due to the complex calculation of KG, theoretical analysis of this algorithm is difficult, and existing results are mostly about the asymptotic performance of it, e. g., consistency, asymptotic sample allocation, etc.
