Retrospective Learning from Interactions
Authors: name: Zizhao Chen, name: Mustafa Omer Gul, name: Yiwei Chen, name: Gloria Geng, name: Anne Wu, name: Yoav Artzi

Multi-turn interactions between large language models (LLMs) and users
naturally include implicit feedback signals. If an LLM responds in an
unexpected way to an instruction, the user is likely to signal it by rephrasing
the request, expressing frustration, or pivoting to an alternative task. Such
signals are task-independent and occupy a relatively constrained subspace of
language, allowing the LLM to identify them even if it fails on the actual
task. This creates an avenue for continually learning from interactions without
additional annotations. We introduce ReSpect, a method to learn from such
signals in past interactions via retrospection. We deploy ReSpect in a new
multimodal interaction scenario, where humans instruct an LLM to solve an
abstract reasoning task with a combinatorial solution space. Through thousands
of interactions with humans, we show how ReSpect gradually improves task
completion rate from 31% to 82%, all without any external annotation.