Papers by Yingtian Tang
From Language to Cognition: How LLMs Outgrow the Human Language Network (2025.emnlp-main)
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Badr AlKhamissi, Greta Tuckute, Yingtian Tang, Taha Osama A Binhuraib, Antoine Bosselut, Martin Schrimpf
| Challenge: | Large language models exhibit remarkable similarity to neural activity in the human language network, but their properties remain unclear. |
| Approach: | They benchmark 34 training checkpoints spanning 300B tokens across 8 different model sizes . they find that brain alignment tracks the development of formal linguistic competence more closely than functional linguistic competency. |
| Outcome: | The results show that large language models exhibit similarity to human language networks . they show that the correlation between next-word prediction and brain alignment fades once models surpass human language proficiency. |
When are Lemons Purple? The Concept Association Bias of Vision-Language Models (2023.emnlp-main)
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| Challenge: | Large-scale vision-language models such as CLIP have shown impressive performance on zero-shot image classification and image-to-text retrieval tasks. |
| Approach: | They propose to use "question text" as input for the text encoder of CLIP to make the prediction harder than it should be. |
| Outcome: | The proposed model treats input as a bag of concepts and attempts to fill in the other missing concept crossmodally, leading to an unexpected zero-shot prediction. |