Do LLMs Benefit From Their Own Words?
Jenny Y. Huang, Leshem Choshen, Ramon Astudillo, Tamara Broderick, and Jacob Andreas

TL;DR
This study investigates whether large language models benefit from including their own previous responses in multi-turn conversations, finding that omitting assistant history often does not harm and can even improve response quality.
Contribution
The paper demonstrates that excluding assistant responses from context can reduce memory usage and sometimes enhance response quality, challenging standard multi-turn prompting practices.
Findings
Omitting assistant responses does not affect response quality in many cases.
Removing assistant history can reduce context length by up to 10x.
Context pollution from previous responses can negatively impact model outputs.
Abstract
Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, we revisit this design choice by asking whether large language models benefit from conditioning on their own prior responses. Using in-the-wild, multi-turn conversations, we compare standard (full-context) prompting with a user-turn-only prompting approach that omits all previous assistant responses, across three open reasoning models and one state-of-the-art model. To our surprise, we find that removing prior assistant responses does not affect response quality on a large fraction of turns. Omitting assistant-side history can reduce cumulative context lengths by up to 10x. To explain this result, we find that multi-turn conversations consist of a substantial proportion (36.4%) of self-contained prompts, and that many follow-up prompts provide…
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Taxonomy
TopicsAI in Service Interactions · Topic Modeling · Artificial Intelligence in Healthcare and Education
