3 papers
cs.LG2025
Weight-sparse transformers have interpretable circuits
Leo Gao, Achyuta Rajaram, Jacob Coxon +3
Finding human-understandable circuits in language models is a central goal of the field of mechanistic interpretability. We train models to have more understandable circuits by con…
cs.CL2025
Considering Length Diversity in Retrieval-Augmented Summarization
Juseon-Do, Jaesung Hwang, Jingun Kwon +2
This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. W…
cs.CL2024
Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions
Angana Borah, Rada Mihalcea
As Large Language Models (LLMs) continue to evolve, they are increasingly being employed in numerous studies to simulate societies and execute diverse social tasks. However, LLMs a…