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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…
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…
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…