From the 1 of 39 linked papers with an AI index.
2 citations · 2 across the 13 of their papers we have counts for
7 papers · 1 filter
CR: Cross-sample Consistency Regularization Mitigates Feature Splitting and Absorption in Sparse Autoencoders
Haoran Jin, Xiting Wang, Shijie Ren +2
Sparse Autoencoders (SAEs) are widely used to interpret large language models by decomposing activations into sparse, human-understandable features, but scaling to large dictionari…
Extreme Region Policy Distillation
Changyu Chen, Xiting Wang, Rui Yan
Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-policy methods discard trajectories…
Semi-Offline Reinforcement Learning for Optimized Text Generation
Changyu Chen, Xiting Wang, Yiqiao Jin +5
In reinforcement learning (RL), there are two major settings for interacting with the environment: online and offline. Online methods explore the environment at significant time co…
To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents
Wei Shi, Ziheng Peng, Sihang Li +4
LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models from three families show high…
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework
Jiaqi Weng, Han Zheng, Hanyu Zhang +6
Sparse autoencoders (SAEs) enable interpretability research by decomposing entangled model activations into monosemantic features. However, under what circumstances SAEs derive mos…
Target Concept Tuning Improves Extreme Weather Forecasting
Shijie Ren, Xinyue Gu, Ziheng Peng +6
Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typicall…