From the 1 of 7 linked papers with an AI index.
7 papers
Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models
Hua-Dong Xiong, Xinyuan Yan, Ji-An Li +3
The paper investigates how large language models think under uncertainty by testing them on two-armed bandit tasks, measuring action preferences, thinking duration, and confidence,…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
Large language models reorganize representational geometry during in-context learning
Hua-Dong Xiong, Li Ji-An, Robert C. Wilson +2
Large language models (LLMs) show remarkable flexibility in adapting to novel tasks without parameter updates, a capacity known as in-context learning (ICL). Prior work has sought…
The Position Curse: LLMs Struggle to Locate the Last Few Items in a List
Zhanqi Zhang, Hua-Dong Xiong, Robert C. Wilson +3
Modern large language models (LLMs) can find a needle in a haystack (locating a single relevant fact buried among hundreds of thousands of irrelevant tokens) with near-saturated ac…
In-context superposition: human-like working memory interference in large language models
Hua-Dong Xiong, Li Ji-An, Jiaqi Huang +3
Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments. This capacity, known as working memory, is fundamental to human…
Language Models Are Capable of Metacognitive Monitoring and Control of Their Internal Activations
Li Ji-An, Hua-Dong Xiong, Robert C. Wilson +2
Large language models (LLMs) can sometimes report the strategies they actually use to solve tasks, yet at other times seem unable to recognize those strategies that govern their be…