Papers by Junan Li

3 papers
Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? (2024.findings-naacl)

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Challenge: Existing approaches to making moral judgments are mostly bottom-up and lack explainability.
Approach: They propose a top-down framework to steer Large Language Models to perform moral reasoning with well-established moral theories.
Outcome: The proposed framework can integrate various moral theories on moral datasets.
Discovery and Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees (2026.acl-long)

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Challenge: Existing approaches to augment Large Language Models (LLMs) with computational capabilities have focused on short Chain-of-thought (CoT) integrating tool-use into long CoT remains underexplored due to the scarcity of training data and the challenge of integrating it without compromising the model’s intrinsic long-chain reasoning.
Approach: They propose a framework that enables spontaneous tool-use during long CoT reasoning without additional human annotation.
Outcome: Experiments on AIME and GPQA-Diamond show that DART significantly outperforms existing methods, successfully harmonizing tool execution with long CoT reasoning.
Speech Discrete Tokens or Continuous Features? A Comparative Analysis for Spoken Language Understanding in SpeechLLMs (2025.emnlp-main)

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Challenge: Speech Large Language Models (SpeechLLMs) have emerged as dominant speech processing approaches.
Approach: They compare self-supervised learning-based discrete and continuous features . they compare performance across six spoken language understanding-related tasks .
Outcome: The proposed models outperform discrete tokens and continuous features in six spoken language understanding-related tasks.

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