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CPIL introduces a two-stage Siamese framework that significantly improves hallucination detection in LLMs by enforcing invariance to surface form variations while leveraging minimal labeled data. By utilizing contrastive pretraining and hard negative mining, the method achieves superior F1 scores on the LLM-AggreFact benchmark with only ~1% of the annotation cost typical for existing classifiers. This approach offers a practical path to reducing reliance on expensive LLM-as-evaluator pipelines for grounding verification.
Cross Paraphrastic Invariance Learning for Hallucination Detection
arXiv:2606.08157v1 Announce Type: new Abstract: Large language models (LLMs) frequently generate hallucinations, which are unsupported by a source document. To avoid costly LLM-as-evaluator pipelines and the heavy annotation demands of existing classifiers, we propose CPIL (Cross Paraphrastic Invariance Learning), a two-stage Siamese framework that maximizes the utility of existing labeled data. Concretely, CPIL constructs informative training pairs by: (i) generating paraphrastic views of each document-claim example as positives, and explicitly aligning their representations to enforce invariance to surface form; and (ii) mining same-document, opposite-label pairs as hard negatives to sharpen document-sensitive decision boundaries. Then CPIL conduct a two-stage model training: Stage 1 performs contrastive pretraining to learn a paraphrase-invariant, grounding-aware embedding space; and Stage 2 attaches a lightweight classifier for binary groundedness. On the LLM-AggreFact benchmark (11 tasks), CPIL surpasses strong baselines concerning F1 scores with only ~1% labeled data, showing its prediction superiority and label efficiency.
Abstract:Large language models (LLMs) frequently generate hallucinations, which are unsupported by a source document. To avoid costly LLM-as-evaluator pipelines and the heavy annotation demands of existing classifiers, we propose CPIL (Cross Paraphrastic Invariance Learning), a two-stage Siamese framework that maximizes the utility of existing labeled data. Concretely, CPIL constructs informative training pairs by: (i) generating paraphrastic views of each document-claim example as positives, and explicitly aligning their representations to enforce invariance to surface form; and (ii) mining same-document, opposite-label pairs as hard negatives to sharpen document-sensitive decision boundaries. Then CPIL conduct a two-stage model training: Stage 1 performs contrastive pretraining to learn a paraphrase-invariant, grounding-aware embedding space; and Stage 2 attaches a lightweight classifier for binary groundedness. On the LLM-AggreFact benchmark (11 tasks), CPIL surpasses strong baselines concerning F1 scores with only ~1% labeled data, showing its prediction superiority and label efficiency.
Computation and Language (cs.CL)
arXiv:2606.08157 [cs.CL]
(or arXiv:2606.08157v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.08157 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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