Papers by Xiaoyuan Zhu
Using Linguistic Entrainment to Evaluate Large Language Models for Use in Cognitive Behavioral Therapy (2025.findings-naacl)
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Mina Kian, Kaleen Shrestha, Katrin Fischer, Xiaoyuan Zhu, Jonathan Ong, Aryan Trehan, Jessica Wang, Gloria Chang, Séb Arnold, Maja Mataric
| Challenge: | Entrainment is a communication process that builds a strong relationship between a mental health therapist and their client. |
| Approach: | They evaluate the linguistic entrainment of an LLM in a mental health dialog setting and compare it to trained therapists and non-expert online peer supporters. |
| Outcome: | The proposed model outperforms humans in a cognitive behavioral therapy setting. |
TS-CLIP: Time Series Understanding by CLIP (2025.emnlp-main)
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| Challenge: | Contrastive Language–Image Pre-training (CLIP) has demonstrated remarkable success in aligning vision and language. |
| Approach: | They propose a synonym bank mechanism that generates synonym embeddings as alignment targets. |
| Outcome: | The proposed approach achieves state-of-the-art (SOTA) performance on 51 datasets. |
Can LLMs Understand Unvoiced Speech? Exploring EMG-to-Text Conversion with LLMs (2025.acl-short)
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| Challenge: | Unvoiced electromyography (EMG) is an effective communication tool for individuals unable to produce vocal speech. |
| Approach: | They propose an EMG adaptor module that maps EMG features to an LLM's input space and achieves an average word error rate of 0.49 on a closed-vocabulary unvoiced EMG-to-text task. |
| Outcome: | The proposed module achieves an average word error rate of 0.49 on a closed-vocabulary unvoiced EMG-to-text task. |
Enhancing LLM-Based Social Bot via an Adversarial Learning Framework (2025.emnlp-main)
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| Challenge: | Social media platforms provide an ideal testbed for large language models that exhibit human-like behavior. |
| Approach: | They propose an LLM-based social **Bot that enhances human-like generative capabilities through an adversarial learning framework. |
| Outcome: | The proposed framework generates human-like content aligned with diverse user profiles . it exhibits strong social responsiveness, more accurately modeling opinion dynamics . |
Learn Your Tokens: Word-Pooled Tokenization for Language Modeling (2023.findings-emnlp)
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| Challenge: | Language models typically tokenize text into subwords, using a deterministic, hand-engineered heuristic of combining characters into longer surface-level strings such as ‘ing’ or whole words. |
| Approach: | They propose a 'learn your tokens' scheme which pooles bytes/characters into word representations and decodes individual characters/bytes per word in parallel. |
| Outcome: | The proposed tokenizer outperforms subword models and byte/character models over the word boundary and outperformed on rare words by a factor of 30! |