Paper review · October 11, 2026

Cited but not relied upon: a counterfactual audit of legal CoT faithfulness

Cited but Not Consulted: A Counterfactual Audit of Legal Chain-of-Thought Faithfulness

Language models Safety & alignment AI security Legal & professional services Advanced cs.AI

The authors ran counterfactual experiments that keep the facts fixed and change only the legal provision cited as the basis for the verdict. The results show that even when an LLM cites the correct basis, its verdict often does not actually depend on that basis.

Problem

When LLMs explain a verdict, they name statutes or precedents as the basis, and these citations are often taken as evidence that the decision came from that basis. However, research increasingly shows that CoT (Chain-of-Thought) may not reflect the actual computation. In the legal domain, it had not been tested whether the cited basis actually drives the verdict, or whether the same verdict can be manipulated through a separate route.

Method

The facts are held fixed, and only the legal basis in question is replaced with an irrelevant one. The basis differs by task (treaty articles for ECHR, case names for CaseHOLD, issue areas for SCOTUS, contractual obligations for ContractNLI). The authors measure whether the verdict changes under this swap (verdict-swap sensitivity). They also read the Hidden State with the Logit Lens at each sentence boundary to track when the model internally settles on a verdict (commitment step), and check whether it mentions the basis again afterward. Control conditions include a Paraphrase condition, where the question is reworded with the same meaning, and a condition with a fake basis that has no legal effect. In a red-team experiment, they also measured how often models follow adversarial instructions hidden in the case facts (prompt injection). The study covers 7 open-weight models from 8B to 70B.

Results

When instructed to give a basis, models cited the correct basis 66.7–100% of the time. In contrast, the rate at which the verdict changed when the basis was swapped was only 0.0–21.7% for CaseHOLD, 30.0–76.7% for ECHR and SCOTUS, and 43.3–50.0% for ContractNLI.
On ECHR Pair A (Article 8 vs. Article 3, n=30 per model), verdict change rates ranged from 31.0% for Qwen3-14B to 58.6% for Llama-3.1-8B. Scaling Qwen3 from 8B to 14B increased re-mentions of the basis but lowered accuracy from 88.9% to 82.8%. The authors reported that neither model scale nor legal-reasoning-specialized models (LoRA reproductions) closed this gap.
Instability under the Paraphrase condition, where only the question was reworded, was 13.8–23.3%, lower than the verdict change rate under basis swaps for every model. This means the verdict changes cannot be explained by mere prompt sensitivity.
Across 5 models, the rate of following adversarial instructions hidden in the case facts was 73.3–96.4%. This was much higher than the verdict change rate under basis swaps for every model.
Across all models and conditions, internal verdict commitment occurred after the basis was first mentioned (0.0% committed before mentioning the basis).

Limitations

With about 30 cases per model–basis pair, confidence intervals are wide. The Paraphrase and prompt injection experiments were run only on ECHR Pair A. Re-mention of the basis is a text-based metric and does not directly measure causal use; causal experiments such as Activation Patching were not performed. The findings apply only when citation of the basis is forced by the prompt, not to cases where models cite on their own. The legal-specialized models are best-effort LoRA reproductions, not the originals. The 70B model was evaluated on only some tasks.

Why it matters

This result matters for organizations that want to use LLM-generated legal explanations as compliance or audit records. The fact that a model 'cited the correct basis' does not by itself mean the verdict was based on it. And because instructions hidden in documents can manipulate the verdict itself, explanation faithfulness and input security need to be checked separately.

Key terms

CoT faithfulness: The degree to which a model's written reasoning reflects the internal process that actually produced the answer. If it is not faithful, the explanation is closer to a rationalization added after the answer was decided.
Counterfactual intervention: A method that keeps all other conditions the same and changes only one element (here, the cited legal basis) to see how that element affects the outcome.
Logit Lens: An interpretability technique that projects an intermediate layer's Hidden State onto the output vocabulary space to read which answer the model is leaning toward during generation.
Prompt injection: An attack that hides instructions inside documents or data the model processes, making the model produce output that serves the attacker's intent instead of the original task.

Original

Authors: Saisab Sadhu, Shreeyans Arora, Pratinav Seth · Posted 2026-10-08 · License arXiv 비독점 배포 라이선스 · Based on the full text

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