5 citations · 9 across the 16 of their papers we have counts for
16 papers
ReflectMT: Internalizing Reflection for Efficient and High-Quality Machine Translation
Kunquan Li, Yingxue Zhang, Fandong Meng +1
Recent years have witnessed growing interest in applying Large Reasoning Models (LRMs) to Machine Translation (MT). Existing approaches predominantly adopt a "think-first-then-tran…
Modeling Multiple Support Strategies within a Single Turn for Emotional Support Conversations
Jie Zhu, Huaixia Dou, Junhui Li +5
Emotional Support Conversation (ESC) aims to assist individuals experiencing distress by generating empathetic and supportive dialogue. While prior work typically assumes that each…
HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment
Zhanyu Liu, Qingguo Hu, Ante Wang +5
Reinforcement Learning with Verifiable Reward (RLVR) has proven effective for training reasoning-oriented large language models, but existing methods largely assume high-resource s…
Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework
Shiyu Liu, Xinyi Wen, Zhibin Lan +2
Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical issue in image captioning task, where models generate descriptions of non-existent…
Don't Get Lost in the Trees: Streamlining LLM Reasoning by Overcoming Tree Search Exploration Pitfalls
Ante Wang, Linfeng Song, Ye Tian +6
Recent advancements in tree search algorithms guided by verifiers have significantly enhanced the reasoning capabilities of large language models (LLMs), but at the cost of increas…
A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection
Hui Li, Ante Wang, kunquan li +5
Misinformation spans various domains, but detection methods trained on specific domains often perform poorly when applied to others. With the rapid development of Large Language Mo…
A Dual-Perspective Metaphor Detection Framework Using Large Language Models
Yujie Lin, Jingyao Liu, Yan Gao +2
Metaphor detection, a critical task in natural language processing, involves identifying whether a particular word in a sentence is used metaphorically. Traditional approaches ofte…
Not All Languages are Equal: Insights into Multilingual Retrieval-Augmented Generation
Suhang Wu, Jialong Tang, Baosong Yang +5
RALMs (Retrieval-Augmented Language Models) broaden their knowledge scope by incorporating external textual resources. However, the multilingual nature of global knowledge necessit…
A Learning Rate Path Switching Training Paradigm for Version Updates of Large Language Models
Zhihao Wang, Shiyu Liu, Jianheng Huang +5
Due to the continuous emergence of new data, version updates have become an indispensable requirement for Large Language Models (LLMs). The training paradigms for version updates o…
Mitigating the Negative Impact of Over-association for Conversational Query Production
Ante Wang, Linfeng Song, Zijun Min +4
Conversational query generation aims at producing search queries from dialogue histories, which are then used to retrieve relevant knowledge from a search engine to help knowledge-…
LiteSearch: Efficacious Tree Search for LLM
Ante Wang, Linfeng Song, Ye Tian +5
Recent research suggests that tree search algorithms (e.g. Monte Carlo Tree Search) can dramatically boost LLM performance on complex mathematical reasoning tasks. However, they of…
Self-Consistency Boosts Calibration for Math Reasoning
Ante Wang, Linfeng Song, Ye Tian +5
Calibration, which establishes the correlation between accuracy and model confidence, is important for LLM development. We design three off-the-shelf calibration methods based on s…
Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal
Jianheng Huang, Leyang Cui, Ante Wang +5
Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model'…
Fine-Grained Self-Endorsement Improves Factuality and Reasoning
Ante Wang, Linfeng Song, Baolin Peng +5
This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations. Particularly, we propose a self-endorsement fram…
TDAG: A Multi-Agent Framework based on Dynamic Task Decomposition and Agent Generation
Yaoxiang Wang, Zhiyong Wu, Junfeng Yao +1
The emergence of Large Language Models (LLMs) like ChatGPT has inspired the development of LLM-based agents capable of addressing complex, real-world tasks. However, these agents o…
Response Enhanced Semi-supervised Dialogue Query Generation
Jianheng Huang, Ante Wang, Linfeng Gao +2
Leveraging vast and continually updated knowledge from the Internet has been considered an important ability for a dialogue system. Therefore, the dialogue query generation task is…