Papers by Mingjie Tang

3 papers
LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)

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Challenge: Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge.
Approach: They propose a recurrent inductive bias that aligns with the recursive nature of programming logic.
Outcome: The proposed model achieves comparable performance to standard dense models with more parameters.
Tuning Less, Prompting More: In-Context Preference Learning Pipeline for Natural Language Transformation (2025.emnlp-main)

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Challenge: Existing approaches to natural language transformation (NLT) tasks face significant challenges, such as the computational costs of leveraging large pre-trained models and the limited generalization ability of fine-tuned smaller models.
Approach: They propose a framework that combines prompting with fine-tuning to enhance smaller models by integrating In-Context Examples from retrieval.
Outcome: The proposed framework outperforms existing methods across MT and TST tasks.
Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution (2026.acl-long)

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Challenge: Existing models of moral evolution must abstract away cognitive processes . et al. (2017): evolution of morality presents a puzzle: natural selection favors selfish .
Approach: They propose an LLM-based agent simulation framework that manipulates cognitive factors to understand moral evolution.
Outcome: The proposed model exploits cognitive realism to explore moral evolution in a hunter-gatherer society.

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