Study: LLMs Perform Well with Reduced Conversation Context
A recent paper on arXiv (2602.24287) explores the advantages of large language models (LLMs) when utilizing the complete history of conversations during multi-turn exchanges. The authors evaluated full-context prompting against four significantly reduced context setups. By examining real-world multi-turn dialogues across three open reasoning models and one leading model, they discovered that the quality of responses remains largely intact even with significant context reduction. Using one-sentence summaries of previous assistant responses or retaining just the latest user-assistant interaction often yields performance comparable to full-context, while requiring about 8 times less context. Additionally, the research found that 36.4% of user turns in these conversations are self-sufficient, with many follow-ups only needing the last user-assistant exchange to be addressed. The full paper can be accessed on arXiv under the identifier 2602.24287.
Key facts
- Paper ID: arXiv:2602.24287
- Study compares full-context prompting to four reduced context configurations
- Analyzed multi-turn conversations across three open reasoning models and one state-of-the-art model
- Aggressive context filtering preserves response quality
- One-sentence summaries of prior assistant turns match full-context performance
- Keeping only the most recent user-assistant exchange also matches performance
- Uses roughly 8x less context
- 36.4% of user turns are self-contained
Entities
Institutions
- arXiv