---
title: "When One LLM Drools, Multi-LLM Collaboration Rules (2026)"
description: "This position paper by Feng et al. (ACL 2026) argues that a single general-purpose LLM cannot… 135 references, cited by 13 papers here."
authors: ["Shangbin Feng", "Wenxuan Ding", "Alisa Liu", "Zifeng Wang", "Weijia Shi", "Yike Wang", "Shannon Zejiang Shen", "Xiaochuang Han", "Hunter Lang", "Chen-Yu Lee", "Tomas Pfister", "Yejin Choi", "Yulia Tsvetkov"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.775/"
doi: "10.18653/v1/2026.acl-long.775"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/feng-2026-when-one-llm-drools-multi/"
---
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# When One LLM Drools, Multi-LLM Collaboration Rules

**Authors:** [Shangbin Feng](https://md.research.elamur.ai/free/en/authors/shangbin-feng/), [Wenxuan Ding](https://md.research.elamur.ai/free/en/authors/wenxuan-ding/), [Alisa Liu](https://md.research.elamur.ai/free/en/authors/alisa-liu/), [Zifeng Wang](https://md.research.elamur.ai/free/en/authors/zifeng-wang/), [Weijia Shi](https://md.research.elamur.ai/free/en/authors/weijia-shi/), [Yike Wang](https://md.research.elamur.ai/free/en/authors/yike-wang/), [Shannon Zejiang Shen](https://md.research.elamur.ai/free/en/authors/shannon-zejiang-shen/), [Xiaochuang Han](https://md.research.elamur.ai/free/en/authors/xiaochuang-han/), [Hunter Lang](https://md.research.elamur.ai/free/en/authors/hunter-lang/), [Chen-Yu Lee](https://md.research.elamur.ai/free/en/authors/chen-yu-lee/), [Tomas Pfister](https://md.research.elamur.ai/free/en/authors/tomas-pfister/), [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/) and [Yulia Tsvetkov](https://md.research.elamur.ai/free/en/authors/yulia-tsvetkov/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.775/) · [PDF](https://aclanthology.org/2026.acl-long.775.pdf) · [DOI 10.18653/v1/2026.acl-long.775](https://doi.org/10.18653/v1/2026.acl-long.775) · [OpenAlex](https://openalex.org/W4407308865)

**Citations:** 1 citations (OpenAlex, 2026-10-14) · [13](https://md.research.elamur.ai/free/en/papers/feng-2026-when-one-llm-drools-multi/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This position paper by Feng et al. (ACL 2026) argues that a single general-purpose LLM cannot reliably handle complex, context-dependent, or subjective settings. It contributes no new dataset or model. Instead, it makes a conceptual case that any one model leaves gaps on three fronts. It misses parts of real-world data, such as recent events, private or copyrighted text, and low-resource languages and dialects. It cannot excel at every skill at once. It reflects a narrow slice of human values, cultures, and developer perspectives. The authors argue that retraining or fine-tuning one model cannot easily close these gaps, because doing so is costly, can trade off other abilities, or raises legal issues. They group scattered prior methods into a taxonomy based on how deeply models share information: API, text, logit, and weight level. They then argue that combining models improves reliability, pluralism, democratization, efficiency, and adaptability. The paper ends with open problems and a research agenda. For low-resource language work, it offers a framework for pairing specialized models with general ones.

## Key points
- **Taxonomy of collaboration.** The four access levels are API-level, text-level, logit-level, and weight-level. The paper names Routing and Cascading as the two main API-level families, which send each query to a suitable model by cost or expertise.
- **No single model leads every leaderboard.** In leaderboards checked on Nov 24, 2024:
  - Gemini topped Chatbot Arena.
  - GPT-4o led HELM.
  - A fine-tuned InternLM was best on Big-Bench Hard in the Open LLM Leaderboard.
  - Leading models would likely trail multilingual LLMs on GlobalBench and DialectBench.
- **Inference cost ordering.** The estimated order is text-level > logit-level > API-level > weight-level. Merged weights are cheapest because the result is deployed as a single model.
- **Open problems.**
  - The field lacks dedicated evaluation for multi-LLM systems.
  - Many methods need extra routers or gates, which limits industry uptake.
  - Interpretability could guide how models are combined.
  - Contributing should be easier for non-experts, for example through an agent that turns natural-language feedback into a new component model.
- **Positioning.** The paper is broader than Yadav et al. (2024a), who focus on model merging and MoE at the weight level. It is more LLM-specific than Du and Kaelbling (2024), who address compositional modeling across several fields.

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

## Institutions

- [Stanford University](https://md.research.elamur.ai/free/en/universities/stanford-university/) — University, Stanford, United States · 506 works · 330,052 citations
- [University of Washington](https://md.research.elamur.ai/free/en/universities/university-of-washington/) — University, Seattle, United States · 402 works · 212,762 citations
- [New York University](https://md.research.elamur.ai/free/en/universities/new-york-university/) — University, New York, United States · 219 works · 103,487 citations
- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 243 works · 76,506 citations

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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,894 works

## Cite

Cite as: Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Shannon Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi and Yulia Tsvetkov. 2026. When One LLM Drools, Multi-LLM Collaboration Rules. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{feng2026when,
  title = {{When One LLM Drools, Multi-LLM Collaboration Rules}},
  author = {Shangbin Feng, Wenxuan Ding, Alisa Liu, Zifeng Wang, Weijia Shi, Yike Wang, Shannon Zejiang Shen, Xiaochuang Han, Hunter Lang, Chen-Yu Lee, Tomas Pfister, Yejin Choi and Yulia Tsvetkov},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.775},
  url = {https://doi.org/10.18653/v1/2026.acl-long.775}
}
```

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## References

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*Updated 2 November 2026*