1 citations · 1 across the 11 of their papers we have counts for
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onPanda: Efficient Annotation of On-Policy Alignment Data for LLMs and Agents via Token-Level Correction
Lei Yang, Mengyin Liu, Jia Wang +9
We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as its core interaction: while r…
ROAM: Robust Organization of Atomic Memories for Agents through Semantic Relations
Jianjie Zheng, Peng Lai, Sijie Cheng +3
Long-term language-model agents rely on external memory across interactions. Atomic memories are particularly useful: their fine-grained semantic boundaries enable precise retrieva…
GUIDE: Generative Unsupervised Chinese Query Correction via Phonetic and Visual Shared-ID Encoding
Lei Yang, Binbin Huang, Jiwei Tan +4
Chinese query correction (CQC) is important for search and query recommendation on content platforms, but supervised methods rely on large annotated correction pairs that are costl…
Beyond Static Rules: Automated Discovery of Latent Vulnerabilities in Text-to-SQL
Hanqing Wang, Yongdong Chi, Jian Yang +4
While Large Language Models (LLMs) have achieved remarkable success in Text-to-SQL tasks, their deployment in real-world environments is hindered by latent reliability issues. Iden…
Local Conflict, Targeted Repair: A Perturbation Analysis of Multi-Domain RL
Lei Yang, Siyu Ding, Deyi Xiong
Reinforcement learning (RL) post-training improves large language models on individual domains but can degrade previously acquired capabilities. We study this interference in mathe…
Representation-Guided Parameter-Efficient LLM Unlearning
Zeguan Xiao, Lang Mo, Yun Chen +4
Large Language Models (LLMs) often memorize sensitive or harmful information, necessitating effective machine unlearning techniques. While existing parameter-efficient unlearning m…
DEP: A Decentralized Large Language Model Evaluation Protocol
Jianxiang Peng, Junhao Li, Hongxiang Wang +15
With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require t…
Thinking Seeds: Restarting On-Policy RL from the Model's Own Past Reasoning States
Lei Yang, Wei Bi, Chenxi Sun +2
On-policy reinforcement learning (RL) for language models trains on the reasoning states visited by the current policy. As training sharpens the policy, some states reached by earl…