64 citations · 67 across the 8 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders
Xu Wang, Bingqing Jiang, Yu Wan +3
Sparse autoencoders (SAEs) have become a standard tool for mechanistic interpretability in autoregressive large language models (LLMs), enabling researchers to extract sparse, huma…
cs.LG2025
Teaching Language Models to Critique via Reinforcement Learning
Zhihui Xie, Jie Chen, Liyu Chen +3
Teaching large language models (LLMs) to critique and refine their outputs is crucial for building systems that can iteratively improve, yet it is fundamentally limited by the abil…