---
title: "Mask-to-Correct+ — paper by Santra et al. (2026)"
description: "The authors propose M2C, a three-stage pipeline that needs neither training nor gold evidence: A masker chooses spans to… 86 references."
authors: ["Payel Santra", "Lavisha Sharma", "Madhusudan Ghosh", "Partha Basuchowdhuri"]
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.175/"
doi: "10.18653/v1/2026.acl-long.175"
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/santra-2026-mask-correct-leveraging-retriever-diversity/"
---
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# Mask-to-Correct+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction

**Authors:** Payel Santra, Lavisha Sharma, Madhusudan Ghosh and Partha Basuchowdhuri · **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.175/) · [PDF](https://aclanthology.org/2026.acl-long.175.pdf) · [DOI 10.18653/v1/2026.acl-long.175](https://doi.org/10.18653/v1/2026.acl-long.175) · [OpenAlex](https://openalex.org/W7166891667)

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

## Summary

Fact correction means rewriting a false claim so that it agrees with evidence while changing as little as possible. Earlier systems mostly learn from hand-annotated claim–evidence pairs. Such data is scarce, biased and transfers poorly across domains, and these systems pay little attention to preserving the claim's meaning. The authors propose M2C, a three-stage pipeline that needs neither training nor gold evidence:

1. A masker chooses spans to hide using Maximal Marginal Relevance (MMR), which weighs how relevant a candidate entity is to the claim against how different it is from entities already chosen.
2. An LLM fills in corrections based on documents fetched by a retriever.
3. A scoring step that mixes factual entailment with surface overlap selects the best candidate.

Since retriever quality varies by query, the extended version M2C+ runs several rankers in parallel and picks the final correction by majority vote. On FEVER and SciFact, both variants outperform every baseline, with SARI improvements of up to 14%. This suggests that lightweight, inference-only correction can compete with supervised approaches.

## Key points

- **Retrievers:** M2C+ ensembles BM25, Contriever, ColBERT, MonoT5 and RM3. RM3 tends to hurt because it depends on pseudo-relevance feedback, so final experiments drop it. Majority voting needs at least three retrievers.
- **Hyperparameters:** For diversity-aware masking (DM), the best settings are a relevance–diversity weight α = 0.3 and m = 10 masked variants. For random masking (RM), a 15% token ratio works best.
- **Cost:** On FEVER, M2C+ is about 2.7–4× slower than M2C with DM and adds only small gains. Both run inference-only within 48GB of GPU memory, and retrieval runs on CPU.
- **Error analysis:** Failure cases include correct candidates being outvoted, negating a claim without supplying the right entity, and the scorer favouring ROUGE-L overlap over a higher DocNLI factual score.
- **Limitations:** The method is memory-heavy, and no larger or commercial LLMs (e.g., GPT-4, Claude) were tested.

*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

Also on the byline: [Indian Association for the Cultivation of Science](https://ror.org/050p6gz73).

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## Cite

Cite as: Payel Santra, Lavisha Sharma, Madhusudan Ghosh and Partha Basuchowdhuri. 2026. Mask-to-Correct+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction. 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{santra2026mask,
  title = {{Mask-to-Correct+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction}},
  author = {Payel Santra, Lavisha Sharma, Madhusudan Ghosh and Partha Basuchowdhuri},
  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.175},
  url = {https://doi.org/10.18653/v1/2026.acl-long.175}
}
```

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*Updated 2 November 2026*