Papers by Yuwei Yan

3 papers
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)

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Challenge: Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news.
Approach: They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately.
Outcome: The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets.
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding? (2025.coling-main)

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Challenge: Recent advances in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks.
Approach: They propose a benchmark to assess LLMs’ code understanding abilities from the perspective of code judging rather than code generation.
Outcome: The proposed benchmark evaluates 12 well-known large language models to determine the correctness of provided code solutions.
A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions (2025.acl-industry)

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Challenge: Existing work on large language models lacks a realistic environment and parallelized framework to support complex interactions between agents and environments.
Approach: They propose a framework that integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents.
Outcome: The proposed framework can support simulations of 30,000 agents faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance increases linearly with the increase of LLM computational resources.

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