14 citations · 18 across the 19 of their papers we have counts for
19 papers
Can LLMs Track Their Output Length? A Dynamic Feedback Mechanism for Precise Length Regulation
Meiman Xiao, Ante Wang, Qingguo Hu +5
Precisely controlling the length of generated text is a common requirement in real-world applications. However, despite significant advancements in following human instructions, La…
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…
Boosting Visual Knowledge-Intensive Training for LVLMs Through Causality-Driven Visual Object Completion
Qingguo Hu, Ante Wang, Jia Song +3
Large Vision-Language Models (LVLMs) have experienced significant advancements in recent years. However, their performance still falls short in tasks requiring deep visual percepti…
Beyond Passive Critical Thinking: Fostering Proactive Questioning to Enhance Human-AI Collaboration
Ante Wang, Yujie Lin, Jingyao Liu +4
Critical thinking is essential for building robust AI systems, preventing them from blindly accepting flawed data or biased reasoning. However, prior work has primarily focused on…
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…
Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study
Yujie Lin, Ante Wang, Moye Chen +4
Recently, inference-time scaling of chain-of-thought (CoT) has been demonstrated as a promising approach for addressing multi-modal reasoning tasks. While existing studies have pre…
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…
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…
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…
Event-Guided Procedure Planning from Instructional Videos with Text Supervision
An-Lan Wang, Kun-Yu Lin, Jia-Run Du +2
In this work, we focus on the task of procedure planning from instructional videos with text supervision, where a model aims to predict an action sequence to transform the initial…
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
Ante Wang, Linfeng Song, Qi Liu +5
Knowledge-aided dialogue response generation aims at augmenting chatbots with relevant external knowledge in the hope of generating more informative responses. The majority of prev…
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings
Shaopeng Lai, Ante Wang, Fandong Meng +6
Dominant sentence ordering models can be classified into pairwise ordering models and set-to-sequence models. However, there is little attempt to combine these two types of models,…
Structural Information Preserving for Graph-to-Text Generation
Linfeng Song, Ante Wang, Jinsong Su +4
The task of graph-to-text generation aims at producing sentences that preserve the meaning of input graphs. As a crucial defect, the current state-of-the-art models may mess up or…