Papers by Runzhe Yang
Prefix Text as a Yarn: Eliciting Non-English Alignment in Foundation Language Model (2024.findings-acl)
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| Challenge: | supervised fine-tuning (SFT) has been a straightforward approach for tailoring the output of foundation large language models (LLMs) to specific preferences. |
| Approach: | They propose a training-free alignment method that uses minimal prior tokens to bridge the foundation LLM and the SFT LLM. |
| Outcome: | The proposed method achieves comparable performance without training on machine translation and part-of-speech tagging across seven languages. |
Who Wrote This? The Key to Zero-Shot LLM-Generated Text Detection Is GECScore (2025.coling-main)
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| Challenge: | Existing methods for detecting LLM-generated text require no training data. |
| Approach: | They propose a black-box zero-shot detection approach that calculates the Grammar Error Correction Score for a given text to differentiate between human-written and LLM-generated texts. |
| Outcome: | The proposed method outperforms current state-of-the-art zero-shot and supervised methods, achieving an average AUROC of 98.62% across XSum and Writing Prompts datasets. |
VisAidMath: Benchmarking Visual-Aided Mathematical Reasoning (2026.acl-long)
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| Challenge: | Existing Large Multi-modal Models lack a robust visual processing capability that is often masked by evaluation metrics that prioritize final-answer accuracy. |
| Approach: | They propose a three-layer evaluation framework that scrutinizes the generation of valid visual aids and the soundness of subsequent reasoning steps. |
| Outcome: | The proposed framework examines the generation of valid visual aids and the soundness of subsequent reasoning steps on state-of-the-art models. |
Rethinking Prompt-based Debiasing in Large Language Model (2025.findings-acl)
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| Challenge: | Existing prompt-based methods for debiasing are often superficial and lack a thorough understanding of complex bias concepts. |
| Approach: | They analyze a BBQ and stereoSet benchmarks to examine the assumption that large language models understand biases. |
| Outcome: | The proposed model misclassified 90% of unbiased content as biased despite high accuracy on BBQ dataset . the proposed model may have been flawed in previous attempts to debiase . |
Improving Dialog Systems for Negotiation with Personality Modeling (2021.acl-long)
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| Challenge: | In this paper, we introduce a framework for generating strategic dialog inspired by the idea of incorporating a theory of mind (ToM) into machines. |
| Approach: | They propose a probabilistic formulation to encapsulate the opponent's personality type during both learning and inference. |
| Outcome: | The proposed model achieves 20% higher dialog agreement rate compared to baselines on a mixed population of opponents. |