Score $\times$ Decoder: A Unified View of Unsupervised Inference-Time Scaling for Hallucination Mitigation
arXiv:2606.00739v1 Announce Type: new Abstract: Large language models hallucinate even when the answer lies within their parameters. While inference-time scaling can surface this latent knowledge, the most effective methods require supervision: a trained verifier or reward model. We ask what can be done with only a base language model: which intrinsic signal best identifies correct outputs, and how should it be decoded? We cast this as a score~$\times$~decoder grid pairing four scores (perplexity, contrastive, power-distribution likelihood, and self-verification) with three decoding families (
The proliferation of powerful LLMs highlights hallucination as a critical bottleneck, prompting urgent research into unsupervised mitigation techniques without relying on additional supervised models.
Improving the trustworthiness and reliability of base LLMs through intrinsic signal analysis can significantly enhance their utility and reduce the cost and complexity of deployment.
The ability to mitigate hallucinations in large language models without external supervision shifts the paradigm towards more self-contained and universally applicable AI systems.
- · AI developers
- · LLM deployment platforms
- · Enterprise AI adopters
- · Companies relying on paid human verification
- · Proprietary hallucination mitigation vendors
Reduced hallucination rates in LLMs lead to more reliable AI applications across various domains.
The cost of deploying and maintaining highly accurate LLM systems decreases, accelerating broader adoption.
Enhanced trust in AI outputs could lead to faster integration of AI into sensitive decision-making processes, potentially impacting white-collar workflows further.
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Read at arXiv cs.LG