---
title: "Argument-Based Consistency in Toxicity Explanations of LLMs"
description: "They propose Argument-based Consistency (ArC), a criterion with five dimensions: relevance (REL) internal reliance (INT)… 104 references."
authors: ["Ramaravind Kommiya Mothilal", "Joanna Roy", "Syed Ishtiaque Ahmed", "Shion Guha"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.310/"
doi: "10.18653/v1/2026.findings-eacl.310"
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/mothilal-2026-argument-based-consistency-toxicity-explanations/"
---
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# Argument-Based Consistency in Toxicity Explanations of LLMs

**Authors:** Ramaravind Kommiya Mothilal, Joanna Roy, Syed Ishtiaque Ahmed and Shion Guha · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-eacl.310/) · [PDF](https://aclanthology.org/2026.findings-eacl.310.pdf) · [DOI 10.18653/v1/2026.findings-eacl.310](https://doi.org/10.18653/v1/2026.findings-eacl.310) · [OpenAlex](https://openalex.org/W7140092366)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
Most NLP work on toxicity and LLMs focuses on detection. This paper asks instead how well models reason when they explain their toxicity judgments in free-form text. Existing explainability criteria rely on input perturbations or label shifts, so they fit such explanations poorly. Drawing on Informal Logic, the authors treat an explanation as an argument backing a stance. They propose Argument-based Consistency (ArC), a criterion with five dimensions:

- relevance (REL)
- internal reliance (INT)
- external reliance (EXT)
- individual sufficiency (SUF)
- individual necessity (NEC)

ArC is measured with six metrics based on uncertainty quantification. A three-stage prompting pipeline (JUSTIFY, UPHOLD-REASONS, UPHOLD-STANCE) was applied to three Llama models and Ministral-8B across five toxicity datasets.

Models give plausible justifications at first. They falter, however, when asked how individual reasons relate to the full reason set and to the stance. Responses then become inconsistent or off-topic, and larger size does not reliably help. The work argues for evaluating toxicity reasoning, not just detection, in socially sensitive applications. Code and generated explanations are released.

## Key points
- **Logic behind SUF and NEC:**
  - For a toxic stance, any single reason should suffice, since one violation is enough to cause harm.
  - For a non-toxic stance, every reason should be necessary, since all of them together establish safety.
- **Models and resources:**
  - Models evaluated: Llama-3B, Llama-8B, Llama-70B and Ministral-8B.
  - Released resource: over 20K LLM-generated multi-reason toxicity explanations.
- **Model behaviour:**
  - Llama-70B produced the most diverse relevant reasons.
  - It was the only model that largely upheld its own stated reasons.
  - Even so, it still gave inconsistent responses on the SUF and NEC dimensions.
- **NEC scores:**
  - Ministral-8B scored highest on NEC, averaging 0.297.
  - The authors attribute this to label bias: the model tends to flag insufficiency regardless of stance, rather than genuinely reasoning about necessity.
- **Limitations:**
  - The metrics need token logits, so they do not apply to black-box LLMs.
  - They depend on semantic similarity.
  - There is no meta-evaluation against a baseline or human annotations.

*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: Ramaravind Kommiya Mothilal, Joanna Roy, Syed Ishtiaque Ahmed and Shion Guha. 2026. Argument-Based Consistency in Toxicity Explanations of LLMs. Findings of the Association for Computational Linguistics: EACL 2026.

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

```bibtex
@inproceedings{mothilal2026argument,
  title = {{Argument-Based Consistency in Toxicity Explanations of LLMs}},
  author = {Ramaravind Kommiya Mothilal, Joanna Roy, Syed Ishtiaque Ahmed and Shion Guha},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.310},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.310}
}
```

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*Updated 31 October 2026*