---
title: "Improving Retrieval-Augmented Generation without"
description: "They propose RePAIR, a planner that maps a failed RAG state (question, retrieved documents, initial answer) directly to a… 38 references."
authors: ["Gongbo Zhang", "Yifan Peng", "Chunhua Weng"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)"
source: "https://aclanthology.org/2026.acl-short.14/"
doi: "10.18653/v1/2026.acl-short.14"
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/zhang-2026-improving-retrieval-augmented-generation-without/"
---
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# Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization

**Authors:** [Gongbo Zhang](https://md.research.elamur.ai/free/en/authors/gongbo-zhang/), [Yifan Peng](https://md.research.elamur.ai/free/en/authors/yifan-peng/) and Chunhua Weng · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short 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-short.14/) · [PDF](https://aclanthology.org/2026.acl-short.14.pdf) · [DOI 10.18653/v1/2026.acl-short.14](https://doi.org/10.18653/v1/2026.acl-short.14) · [OpenAlex](https://openalex.org/W7166845376)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Agentic RAG systems often use a critic model to label what went wrong with an answer, using an error taxonomy, and then choose corrective steps. LLM critics frequently misdiagnose failures, so their fixes can be wasteful or even make answers worse than plain RAG. The authors ask whether this explicit error labelling is needed at all.

They propose RePAIR, a planner that maps a failed RAG state (question, retrieved documents, initial answer) directly to a sequence of high-level operations such as REWRITE, DECOMPOSE, RETRIEVAL, REFINEDOC and GENERATEANSWER. The diagnosis step stays implicit. The planner is trained with DPO on pairs of candidate plans, ranked by the token-level F1 of the answers they produce. Training has two phases:
- **Off-policy phase:** the planner sees oracle correctness labels and flawed reasoning traces.
- **On-policy phase:** the planner sees only a coarse correctness estimate from an LLM judge, which matches deployment conditions.

On NQ, Wizard of Wikipedia and 2WikiMultiHopQA, RePAIR achieves the highest average F1 among all compared methods, beating every agentic baseline. It also uses noticeably shorter action plans. This suggests that coarse correctness signals are useful, while fine-grained error taxonomies may add instability.

## Key points
- RePAIR follows the RAG-Critic (Dong et al., 2025) protocol and evaluation splits on NQ, WoW and 2Wiki, covering single-hop QA, knowledge-grounded dialogue and multi-hop QA.
- Average token-level F1 by method:

  | Method | Avg. F1 |
  |---|---|
  | Standard RAG | 26.2 |
  | RAG-Critic | 28.9 |
  | RePAIR | 30.0 |

  RePAIR's 30.0 is a gain of 3.8 points, or about 13.1%, over standard RAG.
- Several critic-based baselines score below vanilla RAG on average: Self-Refine (19.0), FLARE (15.6), Self-RAG (22.9) and MetaRAG (25.2).
- Ablation of the two-phase training: the off-policy-only variant reaches 28.1 and the on-policy-only variant 28.8. Combining both phases gives the best result.
- The optimized planner calls retrieval, query rewriting, decomposition and document refinement much less often, without losing accuracy.
- Stated limitations: the evaluation covered just three open-domain QA datasets and a predetermined, unchanging operation set. Behaviour under adversarial retrieval was not characterized.

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

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## Cite

Cite as: Gongbo Zhang, Yifan Peng and Chunhua Weng. 2026. Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).

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

```bibtex
@inproceedings{zhang2026improving,
  title = {{Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization}},
  author = {Gongbo Zhang, Yifan Peng and Chunhua Weng},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-short.14},
  url = {https://doi.org/10.18653/v1/2026.acl-short.14}
}
```

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## References

38 references, 23 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 2 November 2026*