12 citations · 21 across the 24 of their papers we have counts for
28 papers
LegalRewardBench: Benchmarking Reward Models for Legal Preference Evaluation
Hui Huang, Jie Zhou, Hongli Zhou +7
Reward models are central to preference alignment for legal tasks, yet existing reward model benchmarks are still primarily centered on general-domain preferences. In this work, we…
RepoMAS: Solving Progressively Specified Tasks with Issue-Driven Multi-Agent Systems
Yuchen Song, Andong Chen, Wenxin Zhu +2
LLM-based multi-agent systems (MASs) have shown strong potential for solving complex tasks, but most assume that task requirements are sufficiently specified before execution. In p…
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…
Long-form RewardBench: Evaluating Reward Models for Long-form Generation
Hui Huang, Yancheng He, Wei Liu +7
The widespread adoption of reinforcement learning-based alignment highlights the growing importance of reward models. Various benchmarks have been built to evaluate reward models i…
Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization
Hongli Zhou, Hui Huang, Rui Zhang +5
Large language model (LLM)-based judges are widely adopted for automated evaluation and reward modeling, yet their judgments are often affected by judgment biases. Accurately evalu…
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…
From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models
Wenxin Zhu, Andong Chen, Yuchen Song +4
With the remarkable success of Multimodal Large Language Models (MLLMs) in perception tasks, enhancing their complex reasoning capabilities has emerged as a critical research focus…
Culture In a Frame: CB as a Comic-Based Benchmark for Multimodal Culturally Awareness
Yuchen Song, Andong Chen, Wenxin Zhu +4
Cultural awareness capabilities have emerged as a critical capability for Multimodal Large Language Models (MLLMs). However, current benchmarks lack progressed difficulty in their…
Beyond Global Emotion: Fine-Grained Emotional Speech Synthesis with Dynamic Word-Level Modulation
Sirui Wang, Andong Chen, Tiejun Zhao
Emotional text-to-speech (E-TTS) is central to creating natural and trustworthy human-computer interaction. Existing systems typically rely on sentence-level control through predef…
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…
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation
Andong Chen, Yuchen Song, Kehai Chen +3
Visual information has been introduced for enhancing machine translation (MT), and its effectiveness heavily relies on the availability of large amounts of bilingual parallel sente…
LLM-based Discriminative Reasoning for Knowledge Graph Question Answering
Mufan Xu, Kehai Chen, Xuefeng Bai +3
Large language models (LLMs) based on generative pre-trained Transformer have achieved remarkable performance on knowledge graph question-answering (KGQA) tasks. However, LLMs ofte…
Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM
Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen +4
Despite being empowered with alignment mechanisms, large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks that can compromise their alignment mechan…
PMoL: Parameter Efficient MoE for Preference Mixing of LLM Alignment
Dongxu Liu, Bing Xu, Yinzhuo Chen +4
Reinforcement Learning from Human Feedback (RLHF) has been proven to be an effective method for preference alignment of large language models (LLMs) and is widely used in the post-…
LLM-based Translation Inference with Iterative Bilingual Understanding
Andong Chen, Kehai Chen, Yang Xiang +5
The remarkable understanding and generation capabilities of large language models (LLMs) have greatly improved translation performance. However, incorrect understanding of the sent…
Dynamic Planning for LLM-based Graphical User Interface Automation
Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen +4
The advent of large language models (LLMs) has spurred considerable interest in advancing autonomous LLMs-based agents, particularly in intriguing applications within smartphone gr…
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…
Large Language Models for Classical Chinese Poetry Translation: Benchmarking, Evaluating, and Improving
Andong Chen, Lianzhang Lou, Kehai Chen +5
Different from the traditional translation tasks, classical Chinese poetry translation requires both adequacy and fluency in translating culturally and historically significant con…