Papers by Wei-Lin Chen
Aligning Large Language Models via Fully Self-Synthetic Data (2026.acl-long)
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| Challenge: | Existing approaches to reinforcement learning from human feedback (RLHF) require expensive human-annotated datasets and proprietary models like GPT-4 to annotate preference pairs. |
| Approach: | They propose a self-synthetic framework for LLM alignment where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself. |
| Outcome: | The proposed framework enhances the model’s chat capabilities on standard benchmarks like AlpacaEval 2.0 while maintaining strong performance on downstream objective tasks. |
ZARA: Improving Few-Shot Self-Rationalization for Small Language Models (2023.findings-emnlp)
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| Challenge: | Recent studies demonstrate great performance gain for self-rationalization by few-shot prompting LMs with rationale-augmented exemplars. |
| Approach: | They propose to leverage explanations for small LMs to improve few-shot self-rationalization by reducing the problem of plausibility judgement to natural language inference. |
| Outcome: | The proposed approach achieves SOTA performance on the FEB benchmark, for both the task accuracy and the explanation metric. |
Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown striking ability to adapt to target tasks with a few input-output demonstrations. |
| Approach: | They propose a framework which bootstraps LMs’ intrinsic capabilities to perform zero-shot ICL. |
| Outcome: | The proposed framework outperforms baselines on 23 BIG-Bench Hard tasks on average accuracy and head-to-head comparison. |
Learning to Generate Explanation from e-Hospital Services for Medical Suggestion (2022.coling-1)
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| Challenge: | Neural models have shown remarkable success in various tasks, however, simply offering the predictions may not satisfy the requirement of end-users. |
| Approach: | They propose a novel model which generates a medical suggestion and provides an explanation as the outline of the reasoning. |
| Outcome: | The proposed model achieves promising performances in both quantitative and human evaluation. |
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text Generation (2023.emnlp-main)
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| Challenge: | Language models often generate fluent and convincing content but can lack consistency with the provided source, resulting in potential inaccuracies. |
| Approach: | They propose a new decoding method that augments the contrastive search framework with context-aware regularization terms to promote tokens that are semantically similar to the provided source while penalizing repetitiveness in the generated text. |
| Outcome: | The proposed method improves faithfulness across various language models while maintaining output diversity comparable to well-performing decoding algorithms. |
Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization (2024.findings-emnlp)
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| Challenge: | Existing literature on leveraging persona in large language models is disorganized and lacks a systematic taxonomy . leveraging peopleas has resurfaced as an ideal lens for adapting LLMs for specific contexts . |
| Approach: | They propose to categorize current research on leveraging persona in large language models . they propose to use a comprehensive survey to categorize existing studies . |
| Outcome: | The proposed framework is a promising framework for tailoring large language models to specific contexts. |