1 citations · 1 across the 5 of their papers we have counts for
6 papers
StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason
Kaiyi Zhang, Ang Lv, Jinpeng Li +4
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving the complex reasoning abilities of large language models (LLMs). However, current RLVR m…
FIRP: Faster LLM inference via future intermediate representation prediction
Pengfei Wu, Jiahao Liu, Zhuocheng Gong +5
Recent advancements in Large Language Models (LLMs) have shown remarkable performance across a wide range of tasks. Despite this, the auto-regressive nature of LLM decoding, which…
E-Bench: Towards Evaluating the Ease-of-Use of Large Language Models
Zhenyu Zhang, Bingguang Hao, Jinpeng Li +2
Most large language models (LLMs) are sensitive to prompts, and another synonymous expression or a typo may lead to unexpected results for the model. Composing an optimal prompt fo…
Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Framework
Xiaoxi Sun, Jinpeng Li, Yan Zhong +2
The advent of large language models (LLMs) has facilitated the development of natural language text generation. It also poses unprecedented challenges, with content hallucination e…
Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration
Pengfei Wu, Jiahao Liu, Zhuocheng Gong +5
Large language models (LLMs) have recently shown remarkable performance across a wide range of tasks. However, the substantial number of parameters in LLMs contributes to significa…
StyleChat: Learning Recitation-Augmented Memory in LLMs for Stylized Dialogue Generation
Jinpeng Li, Zekai Zhang, Quan Tu +3
Large Language Models (LLMs) demonstrate superior performance in generative scenarios and have attracted widespread attention. Among them, stylized dialogue generation is essential…