9 papers
Expanding before Inferring: Enhancing Factuality in Large Language Models through Premature Layers Interpolation
Dingwei Chen, Ziqiang Liu, Feiteng Fang +6
Large Language Models (LLMs) demonstrate remarkable capabilities in text understanding and generation. However, their tendency to produce factually inconsistent outputs, commonly r…
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity
Feiteng Fang, Dingwei Chen, Xiang Huang +10
Currently, most reinforcement learning tasks focus on domains like mathematics and programming, where verification is relatively straightforward. However, in subjective tasks such…
IPBench: Benchmarking the Knowledge of Large Language Models in Intellectual Property
Qiyao Wang, Guhong Chen, Hongbo Wang +20
Intellectual Property (IP) is a highly specialized domain that integrates technical and legal knowledge, making it inherently complex and knowledge-intensive. Recent advancements i…
AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents
Guhong Chen, Liyang Fan, Zihan Gong +8
Current research in LLM-based simulation systems lacks comprehensive solutions for modeling real-world court proceedings, while existing legal language models struggle with dynamic…
STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generation
Jiaming Li, Yukun Chen, Ziqiang Liu +10
Stories are central to human culture, serving to share ideas, preserve traditions, and foster connections. Automatic story generation, a key advancement in artificial intelligence…
CLaSp: In-Context Layer Skip for Self-Speculative Decoding
Longze Chen, Renke Shan, Huiming Wang +6
Speculative decoding (SD) is a promising method for accelerating the decoding process of Large Language Models (LLMs). The efficiency of SD primarily hinges on the consistency betw…