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cs.CL2025
Measuring Aleatoric and Epistemic Uncertainty in LLMs: Empirical Evaluation on ID and OOD QA Tasks
Kevin Wang, Subre Abdoul Moktar, Jia Li +2
Large Language Models (LLMs) have become increasingly pervasive, finding applications across many industries and disciplines. Ensuring the trustworthiness of LLM outputs is paramou…
cs.CL2025
FeatBench: Towards More Realistic Evaluation of Feature-level Code Generation
Haorui Chen, Chengze Li, Jia Li
Evaluating Large Language Models (LLMs) on repository-level feature implementation is a critical frontier in software engineering. However, establishing a benchmark that faithfully…
cs.CL2025
SR: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning
Ruotian Ma, Peisong Wang, Cheng Liu +6
Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale…