2 citations · 5 across the 24 of their papers we have counts for
7 papers · 2 filters
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models
Haoran Lian, Junmin Chen, Wei Huang +8
Recently, Large language models (LLMs) have revolutionized Natural Language Processing (NLP). Pretrained LLMs, due to limited training context size, struggle with handling long tok…
ERABAL: Enhancing Role-Playing Agents through Boundary-Aware Learning
Yihong Tang, Jiao Ou, Che Liu +3
Role-playing is an emerging application in the field of Human-Computer Interaction (HCI), primarily implemented through the alignment training of a large language model (LLM) with…
Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector
Xiaoxue Cheng, Junyi Li, Wayne Xin Zhao +5
Hallucination detection is a challenging task for large language models (LLMs), and existing studies heavily rely on powerful closed-source LLMs such as GPT-4. In this paper, we pr…
Decoding at the Speed of Thought: Harnessing Parallel Decoding of Lexical Units for LLMs
Chenxi Sun, Hongzhi Zhang, Zijia Lin +8
Large language models have demonstrated exceptional capability in natural language understanding and generation. However, their generation speed is limited by the inherently sequen…
Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues
Jiao Ou, Jiayu Wu, Che Liu +3
Aligning large language models (LLMs) with human expectations requires high-quality instructional dialogues, which usually require instructions that are diverse and in-depth. Exist…
Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement
Yihong Tang, Jiao Ou, Che Liu +3
The advent of Large Language Models (LLMs) has propelled dialogue generation into new realms, particularly in the field of role-playing systems (RPSs). While enhanced with ordinary…
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint
Zhipeng Chen, Kun Zhou, Wayne Xin Zhao +4
Reinforcement learning (RL) has been widely used in training large language models (LLMs) for preventing unexpected outputs, eg reducing harmfulness and errors. However, existing R…