I wrote a blog post about my recent thoughts on the scaling in Computational Cognitive Sciences and AI (CogAI). In this blog, I argue that performance scales more easily than insights by pointing out two bottlenecks. Thoughts and comments are welcome! xhb120633.github.io/blog/perform...

I am excited to share that our paper is accepted by @neuripsconf.bsky.social . This is an interesting work that uses tools and insights from comp cog neuroscience to understand LLMs. Nice work by @louannapan.bsky.social and @doctor-bob.bsky.social.

Hanbo Xie@psychboyh.bsky.social · 2y ago

Large Language Models can do a lot of things. But do you know they cannot explore effectively, especially in open-ended tasks? Recently, Lan Pan and I dropped a preprint to investigate how LLMs explore in an open-ended task. arxiv.org/abs/2501.18009

Can Think-Aloud be really useful in understanding human minds? Building on our previous work, we formally propose reopening this old debate, with one of the largest Think-Aloud datasets, "RiskyThought44K," and LLM analysis, showing Think-Aloud can complement to comp cogsci.

Large Language Models can do a lot of things. But do you know they cannot explore effectively, especially in open-ended tasks? Recently, Lan Pan and I dropped a preprint to investigate how LLMs explore in an open-ended task. arxiv.org/abs/2501.18009

Large Language Models Think Too Fast To Explore Effectively

Large Language Models have emerged many intellectual capacities. While numerous benchmarks assess their intelligence, limited attention has been given to their ability to explore, an essential capacit...

arxiv.org

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