Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech (2025.emnlp-main)
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| Challenge: | Existing approaches to decouple LLMs from spoken communication produce suboptimal results due to mismatches between optimal textual and verbal delivery. |
| Approach: | They propose a framework that decouples reasoning from spoken delivery to preserve the full reasoning capacity of LLMs. |
| Outcome: | The proposed framework preserves full reasoning capacity of large language models . it improves speech naturalness and conciseness with minimal impact on reasoning . |
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| Challenge: | Reasoning is a fundamental aspect of human intelligence that plays a crucial role in many intellectual activities. |
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| Challenge: | a new study shows that dialog systems that can hold social talk and make sense of conversational content are not efficient for context-sensitive natural language understanding and reasoning. |
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| Challenge: | emergence of large language models (LLMs) improves capabilities of dialogue systems . but they lack communication skills, which make them more like information seeking tools . |
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| Challenge: | Existing methods that prune or employ early stopping to reduce latency often compromise reasoning reliability. |
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Large Language Models Know What To Say But Not When To Speak (2024.findings-emnlp)
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Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
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Please Translate Again: Two Simple Experiments on Whether Human-Like Reasoning Helps Translation (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) demonstrate strong reasoning capabilities for many tasks, often by explicitly decomposing the task via Chain-of-Thought (CoT) reasoning. |
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The Role of Deductive and Inductive Reasoning in Large Language Models (2025.acl-long)
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Chengkun Cai, Xu Zhao, Haoliang Liu, Zhongyu Jiang, Tianfang Zhang, Zongkai Wu, Jenq-Neng Hwang, Lei Li
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning tasks, yet their reliability in problem-solving remains debatable. |
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