Papers by Xuan Ren
I Learn Better If You Speak My Language: Understanding the Superior Performance of Fine-Tuning Large Language Models with LLM-Generated Responses (2024.emnlp-main)
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| Challenge: | Recent research has demonstrated that a large language model (LLM) can generate training data for another LLM, or for creating supplementary training materials, such as rationales. |
| Approach: | They conduct an in-depth investigation to understand why fine-tuning an LLM with responses generated by a LLM often yields better results than using responses generated from humans. |
| Outcome: | The proposed approach can be used to transfer knowledge from a larger model to a smaller one, or for creating supplementary training materials, such as rationales. |
Ask-before-Plan: Proactive Language Agents for Real-World Planning (2024.findings-emnlp)
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| Challenge: | despite the advancements of large language models, the potential of LLM-powered agents to comprehend ambiguous user instructions is still under exploration. |
| Approach: | They propose a task that requires agents to predict clarification needs based on conversation and agentenvironment interaction and generate a plan to fulfill the user's demands. |
| Outcome: | The proposed framework is based on a new ask-before-plan benchmark dataset. |
Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
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Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
| Approach: | They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
| Outcome: | The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
TriageAgent: Towards Better Multi-Agents Collaborations for Large Language Model-Based Clinical Triage (2024.findings-emnlp)
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| Challenge: | escalation in emergency department patient visits poses challenges to efficient clinical management . Currently, hospitals rely on human experts to review clinical notes and determine case urgency . |
| Approach: | a team of researchers develop a multi-agent framework to enhance collaborative decision-making in clinical triage. |
| Outcome: | The proposed framework outperforms state-of-the-art LLM-based methods on three clinical triage test sets. |
Efficiently Selecting Response Generation Strategies for Synthetic Data Construction by Self-Aligned Perplexity (2025.findings-emnlp)
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| Challenge: | Using a small sample of data, we find that perplexity is suboptimal in characterizing “familiarity” . |
| Approach: | They propose a method that assesses a small subset of generated data to estimate suitability for a specific target LLM. |
| Outcome: | The proposed method assesses a small subset of generated data to estimate suitability for a specific target LLM. |