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Large language models (LLMs) struggle on processing complicated observationsin interactive decision making tasks. To alleviate this issue, we propose asimple hierarchical prompting approach. Diverging from previous promptingapproaches that always put the full observation (e.g. a web page) to theprompt, we propose to first construct an action-aware observation which is morecondensed and relevant with a dedicated SUMMARIZER prompt. The ACTOR promptthen predicts the next action based on the summarized observation. While ourmethod has broad applicability, we particularly demonstrate its efficacy in thecomplex domain of web navigation where a full observation often containsredundant and irrelevant information. Our approach outperforms the previousstate-of-the-art prompting mechanics by 6.2% on task success rate,demonstrating its potential on interactive decision making tasks with longobservation traces.

Hierarchical Prompting Assists Large Language Model on Web Navigation