8 citations · 19 across the 25 of their papers we have counts for
4 papers · 1 filter
Does Thinking Help Fairness? Reasoning Tokens Resolve Some Biases but Create More
Deng Pan, Joe Germino, Yihong Ma +4
Thinking in reasoning language models (RLMs) has been subject to debate on whether it resolves or amplifies bias. Prior works have shown competing conclusions in both directions. U…
The Reasoning Boundary Paradox: How Reinforcement Learning Constrains Language Models
Phuc Minh Nguyen, Chinh D. La, Duy M. H. Nguyen +3
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key method for improving Large Language Models' reasoning capabilities, yet recent evidence suggests it may p…
Geometry of Knowledge Allows Extending Diversity Boundaries of Large Language Models
Mateusz Bystroński, Doheon Han, Nitesh V. Chawla +1
Starting from the hypothesis that knowledge in semantic space is organized along structured manifolds, we argue that this geometric structure renders the space explorable. By trave…
Context Attribution with Multi-Armed Bandit Optimization
Deng Pan, Keerthiram Murugesan, Ting Hua +2
Understanding which parts of the retrieved context contribute to a large language model's generated answer is essential for building interpretable and trustworthy retrieval-augment…