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Chain of Agents: Large Language Models Collaborating on Long-Context Tasks

Paper · 2024 · Yusen Zhang, Ruoxi Sun, Yanfei Chen et al.

Summary

LLMs handle very long inputs poorly, and both standard fixes fall short. Shrinking the input through truncation or RAG may discard the passages that matter. Enlarging the context window leaves the model struggling to attend to relevant content, as in the "lost in the middle" effect. The authors propose Chain-of-Agents (CoA), a training-free, task-agnostic framework that cuts the input into chunks fitting a model's window. Worker agents process these chunks sequentially. Each one receives a natural-language note (a "communication unit") from its predecessor, adds evidence from its own chunk and passes it on. A final manager agent turns the accumulated note into the answer. The whole input is covered, yet every agent sees only a short context, and cost grows as nk instead of n². The authors tested CoA on nine question answering, summarization and code completion datasets with six LLMs from the PaLM 2, Gemini and Claude 3 families. It outperformed RAG, Full-Context and two purpose-built multi-agent baselines on all datasets, by up to 10%. The results show that agent communication can substitute for ever-longer context windows.

Key points

  • CoA works in two stages: a chain of workers reads the chunks in order and passes along task-specific communication units (evidence for QA, running summaries for summarization, code summaries for completion), and then a manager agent produces the output.
  • The evaluation covered datasets including HotpotQA, MuSiQue, NarrativeQA, Qasper, QuALITY, QMSum and RepoBench-P. CoA beat RAG, Full-Context (Vanilla), and hierarchical and result-merging multi-agent baselines.
  • In the ablation, removing the manager reduced MuSiQue from 37.09 to 26.79. Left-to-right reading generally outperformed Right-to-Left and random Permutation orders.
  • On the "lost in the middle" test, CoA's performance spread was 4.89 (±1.91), compared with 6.13 (±2.17) for the Vanilla model.
  • Multi-path variants (Bi-direction, Self-Consistency, Permutation, combined via voting or an LLM judge) improved results further, with 5-way Permutation helping most. The large gap to the oracle leaves room for future work.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksComputer Vision and Pattern Recognition · 3,486 works

Cite

Cite as Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang and Sercan O. Arik. 2024. Chain of Agents: Large Language Models Collaborating on Long-Context Tasks. arXiv:2406.02818.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2406.02818 · arXiv 2406.02818

@misc{zhang2024chain,
  title = {{Chain of Agents: Large Language Models Collaborating on Long-Context Tasks}},
  author = {Yusen Zhang, Ruoxi Sun, Yanfei Chen, Tomas Pfister, Rui Zhang and Sercan O. Arik},
  year = {2024},
  doi = {10.48550/arxiv.2406.02818},
  eprint = {2406.02818},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2406.02818}
}

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