Papers by Zhenyu Bi
CROSSAGENTIE: Cross-Type and Cross-Task Multi-Agent LLM Collaboration for Zero-Shot Information Extraction (2025.findings-acl)
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| Challenge: | Large language models struggle with producing structured output while maintaining accuracy in zero-shot information extraction (IE) |
| Approach: | They propose a multi-agent framework that enhances zero-shot IE through multi-task collaboration. |
| Outcome: | CROSSAGENTIE outperforms state-of-the-art models in structured prediction . the framework significantly reduces inference cost while preserving accuracy . |
DEBATE, TRAIN, EVOLVE: Self‐Evolution of Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Large language models have improved significantly in reasoning through extensive training on massive datasets. |
| Approach: | They propose a ground truth-free training framework that uses multi-agent debate traces to evolve a single language model. |
| Outcome: | The proposed framework achieves 8.92% accuracy gain on the GSM-PLUS dataset. |
AI for Science in the Era of Large Language Models (2024.emnlp-tutorials)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated significant prowess in tasks involving natural language, such as translating languages, constructing chatbots, and answering questions. |
| Approach: | This tutorial explores the application of large language models to three crucial categories of scientific data: 1) textual data, 2) biomedical sequences, and 3) brain signals. |
| Outcome: | This tutorial will explore the application of large language models to three crucial categories of scientific data. |