Papers by Yongyuan Li
Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood (2024.emnlp-main)
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| Challenge: | Existing methods for detecting modelgenerated texts from human texts are limited by the fact that absolute likelihood values of texts are bound to certain linguistic and cognitive constraints. |
| Approach: | They propose to use relative likelihood values instead of absolute ones to extract useful features from the spectrum-view of likelihood for the human-model text detection task. |
| Outcome: | The proposed method can reveal subtle differences between human and model languages, which find theoretical roots in psycholinguistics studies. |
Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)
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| Challenge: | Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences. |
| Approach: | They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal . |
| Outcome: | The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset . |
Think in Sentences: Explicit Sentence Boundaries Enhance Language Model’s Capabilities (2026.acl-long)
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| Challenge: | Existing studies focus on dummy tokens but fail to leverage the inherent sentence-level structure of natural language. |
| Approach: | They propose a method that inserts delimiters at sentence boundaries to enhance large language models' capabilities. |
| Outcome: | The proposed method improves performance on 7B LLMs to 600B Deepseek-V3 with 7.7% gains on GSM8k and 12.5% on DROP. |