Being a computer scientist who refuses to find anything about LLMs interesting right now is a bit like being a geneticist who refuses to find anything interesting about the recently opened Jurassic Park. Skeptical geneticist: "pfft, it's ju…
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I have a lot of mixed feelings about AI and LLM technology. I’m fascinated by its effect on our profession, excited by the potential gains in productivity - and thus the products we could rapidly build. On the other hand, I’m fearful of the…
This paper develops a framework called GAVEL that helps long-horizon language models (LLMs) plan tasks for robots more effectively by predicting and repairing potential errors. Practitioners caring about reliable and efficient robot planning might find this approach useful.
This paper develops a framework to evaluate the social reasoning of large language models (LLMs) in a more realistic setting, by simulating interactions between the LLM and users who provide feedback on the LLM's predictions. Practitioners might care about this research because it helps improve the social reasoning of LLMs, which are increasingly used for advice and decision-making.