1 citations · 2 across the 6 of their papers we have counts for
3 papers · 1 filter
Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization
Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li +8
Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phe…
Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models
Abhishek Kumar, Robert Morabito, Sanzhar Umbet +2
As the use of Large Language Models (LLMs) becomes more widespread, understanding their self-evaluation of confidence in generated responses becomes increasingly important as it is…
Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models
Abhishek Kumar, Sarfaroz Yunusov, Ali Emami
Research on Large Language Models (LLMs) has often neglected subtle biases that, although less apparent, can significantly influence the models' outputs toward particular social na…