1 citations · 1 across the 6 of their papers we have counts for
6 papers
Do VLMs Align Better with Humans than LLMs during Natural Reading?
Jinzhou Wu, Zhengwu Ma, Jixing Li +2
Large language models have become increasingly useful computational models of human language processing, but it remains open whether vision-language learning makes text representat…
ExpLang: Improved Exploration and Exploitation in LLM Reasoning with On-Policy Thinking Language Selection
Changjiang Gao, Zixian Huang, Kaichen Yang +3
Current large reasoning models (LRMs) have shown strong ability on challenging tasks after reinforcement learning (RL) based post-training. However, previous work mainly focuses on…
Brains and language models converge on a shared conceptual space across different languages
Zaid Zada, Samuel A Nastase, Jixing Li +1
Human languages differ widely in their forms, each having distinct sounds, scripts, and syntax. Yet, they can all convey similar meaning. Do different languages converge on a share…
Multilingual Pretraining and Instruction Tuning Improve Cross-Lingual Knowledge Alignment, But Only Shallowly
Changjiang Gao, Hongda Hu, Peng Hu +3
Despite their strong ability to retrieve knowledge in English, current large language models show imbalance abilities in different languages. Two approaches are proposed to address…
Measuring Meaning Composition in the Human Brain with Composition Scores from Large Language Models
Changjiang Gao, Jixing Li, Jiajun Chen +1
The process of meaning composition, wherein smaller units like morphemes or words combine to form the meaning of phrases and sentences, is essential for human sentence comprehensio…
Roles of Scaling and Instruction Tuning in Language Perception: Model vs. Human Attention
Changjiang Gao, Shujian Huang, Jixing Li +1
Recent large language models (LLMs) have revealed strong abilities to understand natural language. Since most of them share the same basic structure, i.e. the transformer block, po…