Papers by Zichun Yu
Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022.coling-1)
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| Challenge: | Prompting has shown to be sample efficient compared to fine-tuning with pre-trained models. |
| Approach: | They propose a fully automatic prompting method that uses natural language prompts on sequence-to-sequence models and a beam search method to generate a large amount of label sequence candidates. |
| Outcome: | The proposed method significantly outperforms other no-manual-design methods on single label words and generates large amount of label sequence candidates. |
Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search (2026.acl-long)
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| Challenge: | Large Language Model (LLM) based multi-agent systems (MAS) have high potential for tackling complex tasks through collaborative intelligence. |
| Approach: | They propose a framework that incorporates influence scores to guide tree search and data selection in data synthesis. |
| Outcome: | The proposed framework incorporates influence scores to guide tree search and data selection in data synthesis. |
Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-In (2023.acl-long)
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| Challenge: | Prior work on retrieval augmentation fine-tuned the retriever and the LM, making them closely coupled. |
| Approach: | They propose a generic retrieval plug-in that can be used to fine-tune retrieval augmentation and a LM to learn a user's preferences. |
| Outcome: | The proposed retriever improves the generalization of large language models on the MMLU and PopQA datasets by learning LM’s preferences from a known source LM . |