Papers by Jixing Li
Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models (2024.acl-long)
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| Challenge: | Existing computational metric to quantify extent of meaning composition is lacking . despite extensive neurolinguistic research, understanding how meaning is constructed is difficult . |
| Approach: | They propose a computational metric to quantify the degree of meaning composition . they use key-value memory interpretation of transformer feed-forward network blocks to study meaning composition. |
| Outcome: | Experimental results show that the metric correlates with brain clusters associated with word frequency, structural processing, and general sensitivity to words, suggesting multifaceted nature of meaning composition during human sentence comprehension. |
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly (2024.naacl-long)
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| Challenge: | Current large language models show imbalance abilities in different languages . authors propose two approaches to improve cross-lingual knowledge alignment . |
| Approach: | They propose a framework to assess cross-lingual knowledge alignment of large language models . they propose multilingual pretraining and multilingual instruction tuning to address this problem . |
| Outcome: | The proposed framework assesses the cross-lingual knowledge alignment of LLMs in performance, consistency and conductivity levels. |
Traces in the Brain: Neural Evidence for Syntactic Movement in English and Chinese (2026.findings-acl)
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Yuhan Huang, Zhengwu Ma, Yuqi Jin, Beth Chan, Zheng Shen, Jackie Yan-Ki Lai, John T. Hale, Jixing Li
| Challenge: | Syntactic movement is a core concept in generative linguistics to account for word-order variation and long-distance dependencies. |
| Approach: | They annotated every sentence in the audiobook The Little Prince using X-bar style tree annotations. |
| Outcome: | The proposed model shows that deep structure significantly predicts neural responses in English but not in Chinese. |
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention (2023.findings-emnlp)
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| Challenge: | Recent large language models (LLMs) have shown strong abilities to understand natural language, but how these factors affect the models’ language perception is unclear. |
| Approach: | They compare the self-attention of several existing large language models in different sizes to assess the effect of scaling and instruction tuning on language perception. |
| Outcome: | The proposed models are closer to non-native speakers than native speakers in attention, suggesting a sub-optimal language perception of all models. |
Computational Linguistics for Brain Encoding and Decoding: Principles, Practices and Beyond (2024.acl-tutorials)
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| Challenge: | This tutorial will explore the potential of computational linguistics to help understand brain language processing. |
| Approach: | This tutorial will explore the principles and practices of using computational linguistics methods for brain encoding and decoding. |
| Outcome: | This tutorial will explore the principles and practices of using computational linguistics methods for brain encoding and decoding. |