What question did this study set out to answer?

This research aims to present a framework that minimizes hallucinations in small language models during inference.

February 9, 20260 citationsOpen Access

Active Minimax Decoder: Reducing Hallucinations in Small Language Models via Binary Adversarial Verification

Key Points

This research aims to present a framework that minimizes hallucinations in small language models during inference.
Introduced the Active Minimax Decoder (AMD) for inference time application.
Implemented a three-agent architecture comprising a Generator, Adversary, and Arbiter.
Evaluated the effectiveness of AMD using TruthfulQA with 817 questions.
AMD achieves a 0.61% hallucination rate, significantly lower than competitor models.
Demonstrates a 96% reduction in hallucinations compared to its baseline.
AMD achieves 97.5% accuracy when electing to answer.

Abstract

We introduce the Active Minimax Decoder (AMD), a training-free, inference-time framework that reduces hallucinations in Small Language Models through binary adversarial verification. AMD employs a three-agent architecture: a Generator produces candidate responses, an Adversary identifies factual inconsistencies, and an Arbiter renders binary accept/abstain decisions. On TruthfulQA (817 questions), AMD enables SmolLM2-360M (0.36B parameters) to achieve a 0.61% hallucination rate ,17x lower than Gemini-2.0-Flash in vanilla mode and 96% reduction versus its own baseline. When AMD elects to answer, it achieves 97.5% accuracy. Our adversary ablation study reveals that adversary capability, rather than size, determines verification quality: LFM2-350M outperforms Qwen-1.5B as an adversary despite being 4x smaller.

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