1 citations · 1 across the 11 of their papers we have counts for
12 papers
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…
From Curated Data to Scalable Models: Continual Pre-training of Dense and MoE Large Language Models for Tibetan
Lei Yang, Leiyu Pan, Bojian Xiong +14
Large language models (LLMs) have achieved remarkable success across a wide range of natural language processing tasks, yet their performance remains heavily biased toward high-res…
ProBench: Benchmarking Large Language Models in Competitive Programming
Lei Yang, Renren Jin, Ling Shi +3
With reasoning language models such as OpenAI-o3 and DeepSeek-R1 emerging, large language models (LLMs) have entered a new phase of development. However, existing benchmarks for co…
DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Search
Lei Yang, Shaoyang Xu, Jianxiang Peng +2
Large language models (LLMs) based on the Transformer architecture usually have their context length limited due to the high training cost. Recent advancements extend the context w…
ChineseSafe: A Chinese Benchmark for Evaluating Safety in Large Language Models
Hengxiang Zhang, Hongfu Gao, Qiang Hu +7
With the rapid development of Large language models (LLMs), understanding the capabilities of LLMs in identifying unsafe content has become increasingly important. While previous w…