activity
20232026
most citedAnalyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

4 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Bridging Inference-Time Scaling and Episodic Memory with Action-Centric Graphs

Xu Zheng, Chaohao Lin, Zhuomin Chen +4

Recent advancements in inference-time scaling have significantly unlocked the complex reasoning capabilities of Large Language Models~(LLMs). However, for agents, these approaches…

cs.CL2024

RATT: A Thought Structure for Coherent and Correct LLM Reasoning

Jinghan Zhang, Xiting Wang, Weijieying Ren +3

Large Language Models (LLMs) gain substantial reasoning and decision-making capabilities from thought structures. However, existing methods such as Tree of Thought and Retrieval Au…

cs.CV2024

Gradient-Aware Logit Adjustment Loss for Long-tailed Classifier

Fan Zhang, Wei Qin, Weijieying Ren +3

In the real-world setting, data often follows a long-tailed distribution, where head classes contain significantly more training samples than tail classes. Consequently, models tra…

cs.LG20244 cited

Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

Weijieying Ren, Xinlong Li, Lei Wang +2

Existing research has shown that large language models (LLMs) exhibit remarkable performance in language understanding and generation. However, when LLMs are continuously fine-tune…

cs.LG2023

T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data

Weijieying Ren, Tianxiang Zhao, Wei Qin +1

In many real-world scenarios, distribution shifts exist in the streaming data across time steps. Many complex sequential data can be effectively divided into distinct regimes that…