3 citations · 4 across the 9 of their papers we have counts for
9 papers
: Unlocking LLM Reasoning via Reinforcement Learning with Re-solving
Pinzheng Wang, Shuli Xu, Juntao Li +4
Reinforcement learning with verifiable rewards (RLVR) has shown promise in enhancing the reasoning performance of large language models (LLMs) by increasing test-time compute. Howe…
Improving Rationality in the Reasoning Process of Language Models through Self-playing Game
Pinzheng Wang, Juntao Li, Zecheng Tang +2
Large language models (LLMs) have demonstrated considerable reasoning abilities in various tasks such as mathematics and coding. However, recent studies indicate that even the best…
Revealing and Mitigating Over-Attention in Knowledge Editing
Pinzheng Wang, Zecheng Tang, Keyan Zhou +3
Large Language Models have demonstrated superior performance across a wide range of tasks, but they still exhibit undesirable errors due to incorrect knowledge learned from the tra…
OpenBA-V2: Reaching 77.3% High Compression Ratio with Fast Multi-Stage Pruning
Dan Qiao, Yi Su, Pinzheng Wang +18
Large Language Models (LLMs) have played an important role in many fields due to their powerful capabilities.However, their massive number of parameters leads to high deployment re…
Rethinking Negative Instances for Generative Named Entity Recognition
Yuyang Ding, Juntao Li, Pinzheng Wang +3
Large Language Models (LLMs) have demonstrated impressive capabilities for generalizing in unseen tasks. In the Named Entity Recognition (NER) task, recent advancements have seen t…
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
Juntao Li, Zecheng Tang, Yuyang Ding +9
Large language models (LLMs) with billions of parameters have demonstrated outstanding performance on various natural language processing tasks. This report presents OpenBA, an ope…
CMD: a framework for Context-aware Model self-Detoxification
Zecheng Tang, Keyan Zhou, Juntao Li +5
Text detoxification aims to minimize the risk of language models producing toxic content. Existing detoxification methods of directly constraining the model output or further train…
Can Diffusion Model Achieve Better Performance in Text Generation? Bridging the Gap between Training and Inference!
Zecheng Tang, Pinzheng Wang, Keyan Zhou +3
Diffusion models have been successfully adapted to text generation tasks by mapping the discrete text into the continuous space. However, there exist nonnegligible gaps between tra…
UFNRec: Utilizing False Negative Samples for Sequential Recommendation
Xiaoyang Liu, Chong Liu, Pinzheng Wang +5
Sequential recommendation models are primarily optimized to distinguish positive samples from negative ones during training in which negative sampling serves as an essential compon…