3 papers
cs.LG2026
DevBench: A Realistic, Developer-Informed Benchmark for Code Generation Models
Adarsh Kumarappan, Pareesa Ameneh Golnari, Wen Wen +5
DevBench is a telemetry-driven benchmark designed to evaluate Large Language Models (LLMs) on realistic code completion tasks. It includes 1,800 evaluation instances across six pro…
cs.CL2025
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
cs.CL2025
Segmenting Text and Learning Their Rewards for Improved RLHF in Language Model
Yueqin Yin, Shentao Yang, Yujia Xie +5
Reinforcement learning from human feedback (RLHF) has been widely adopted to align language models (LMs) with human preference. Prior RLHF works typically take a bandit formulation…