59 citations · 142 across the 51 of their papers we have counts for
6 papers · 1 filter
What Do Agents Learn from Trajectory-SFT: Semantics or Interfaces?
Weizheng Gu, Chengze Li, Zhuohao Yu +6
Large language models are increasingly evaluated as interactive agents, yet standard agent benchmarks conflate two qualitatively distinct sources of success: semantic tool-use and…
Mitigating Spurious Correlations with Causal Logit Perturbation
Xiaoling Zhou, Wei Ye, Rui Xie +1
Deep learning has seen widespread success in various domains such as science, industry, and society. However, it is acknowledged that certain approaches suffer from non-robustness,…
Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective
Deyang Kong, Qi Guo, Xiangyu Xi +5
Reinforcement learning exhibits potential in enhancing the reasoning abilities of large language models, yet it is hard to scale for the low sample efficiency during the rollout ph…
Enhancing In-Context Learning via Implicit Demonstration Augmentation
Xiaoling Zhou, Wei Ye, Yidong Wang +4
The emergence of in-context learning (ICL) enables large pre-trained language models (PLMs) to make predictions for unseen inputs without updating parameters. Despite its potential…
Boosting Model Resilience via Implicit Adversarial Data Augmentation
Xiaoling Zhou, Wei Ye, Zhemg Lee +2
Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially t…
TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training
Chaoya Jiang, Wei ye, Haiyang Xu +4
Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic modalities. Due to no…