How can agents like LLMs become decision-making partners for humans?

💬 Excited to share a new paper + suite of envs for 𝘥𝘦𝘤𝘪𝘴𝘪𝘰𝘯-𝘰𝘳𝘪𝘦𝘯𝘵𝘦𝘥 𝘥𝘪𝘢𝘭𝘰𝘨𝘶𝘦𝘴, where agents + humans collab to solve hard everyday problems. [1/n]

Site: collaborative-dialogue.github.io A lot of everyday problems involve making decisions with messy constraints—from researching a laptop to buy to prioritizing a company roadmap.

Agents could help us make these decisions! But they need to integrate the fuzzy real-world knowledge and preferences that we know.

How can agents infer what people want from what they say?

In our new paper at #acl2022nlp w/ @dan_fried, Dan Klein, and @ancadianadragan, we learn preferences from language by reasoning about how people communicate in context.

Paper: arxiv.org/abs/2204.02515
[1/n] @dan_fried @ancadianadragan We’d like AI agents that not only follow our instructions (“book this flight”), but learn to generalize to what to do in new contexts (know what flights I prefer from our past interactions and book on my behalf) — i.e., learn *rewards* from language. [2/n]