2 citations · 2 across the 9 of their papers we have counts for
10 papers
User-Aware Active Knowledge Acquisition for Emotional Support Dialogue
Mufan Xu, Kehai Chen, Jiahao Hu +4
Emotional support plays an important role in dialogue systems, and its success depends on adapting to a user's evolving and implicit needs across multi-turn interactions while leve…
Beyond Token-Level Policy Gradients for Complex Reasoning with Large Language Models
Mufan Xu, Kehai Chen, Xuefeng Bai +4
Existing policy-gradient methods for auto-regressive language models typically select subsequent tokens one at a time as actions in the policy. While effective for many generation…
Thinking with Comics: Enhancing Multimodal Reasoning through Structured Visual Storytelling
Andong Chen, Wenxin Zhu, Qiuyu Ding +3
Chain-of-Thought reasoning has driven large language models to extend from thinking with text to thinking with images and videos. However, different modalities still have clear lim…
Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning
Yihong Tang, Kehai Chen, Muyun Yang +4
The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interact…
Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory
Hongli Zhou, Hui Huang, Ziqing Zhao +10
The evaluation of large language models (LLMs) via benchmarks is widespread, yet inconsistencies between different leaderboards and poor separability among top models raise concern…
DuplexMamba: Enhancing Real-time Speech Conversations with Duplex and Streaming Capabilities
Xiangyu Lu, Wang Xu, Haoyu Wang +5
Real-time speech conversation is essential for natural and efficient human-machine interactions, requiring duplex and streaming capabilities. Traditional Transformer-based conversa…
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning
Mufan Xu, Gewen Liang, Kehai Chen +5
Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, exi…
Evaluating o1-Like LLMs: Unlocking Reasoning for Translation through Comprehensive Analysis
Andong Chen, Yuchen Song, Wenxin Zhu +4
The o1-Like LLMs are transforming AI by simulating human cognitive processes, but their performance in multilingual machine translation (MMT) remains underexplored. This study exam…
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training
Hui Huang, Jiaheng Liu, Yancheng He +5
Complex instruction-following with elaborate constraints is imperative for Large Language Models (LLMs). While existing methods have constructed data for complex instruction alignm…
Mitigating the Bias of Large Language Model Evaluation
Hongli Zhou, Hui Huang, Yunfei Long +5
Recently, there has been a trend of evaluating the Large Language Model (LLM) quality in the flavor of LLM-as-a-Judge, namely leveraging another LLM to evaluate the current output…