LLMs语义幻觉的不确定性量化：基于量子张量网络的方法
Semantic Uncertainty Quantification of Hallucinations in LLMs: A Quantum Tensor Network Based Method
AI总结：
基于量子张量网络的方法用于量化LLMs语义幻觉的不确定性，通过熵最大化策略提升幻觉检测的鲁棒性和准确性。
摘要：
大型语言模型（LLMs）具有强大的生成能力，但容易产生不准确的叙述，即在相同提示下随意变化的流畅但不可靠的输出。利用基于量子张量网络的流程，我们提出了一种受量子物理启发的不确定性量化框架，用于在语义等价基础上对token序列概率中的随机不确定性进行建模，以对LLM生成进行聚类。这提供了一种系统且可解释的幻觉检测方案。我们进一步引入了熵最大化策略，优先选择高确定性、语义一致的输出，并突出熵区域中LLM决策可能不可靠的区域，为需要人类监督的情况提供实用指南。我们在不同生成长度和量化级别下评估了该方案的鲁棒性，展示了在资源受限部署中该方法仍保持可靠。在TriviaQA、NQ、SVAMP和SQuAD多个架构上进行了116次实验，包括Mistral-7B、Mistral-7B-instruct、Falcon-rw-1b、LLaMA-3.2-1b、LLaMA-2-13b-chat、LLaMA-2-7b-chat、LLaMA-2-13b和LLaMA-2-7b，展示了在AUROC和AURAC上优于现有基准的一致改进。
英文摘要：
Large language models (LLMs) exhibit strong generative capabilities but remain vulnerable to confabulations, fluent yet unreliable outputs that vary arbitrarily even under identical prompts. Leveraging a quantum tensor network based pipeline, we propose a quantum physics inspired uncertainty quantification framework that accounts for aleatoric uncertainty in token sequence probability for semantic equivalence based clustering of LLM generations. This offers a principled and interpretable scheme for hallucination detection. We further introduce an entropy maximization strategy that prioritizes high certainty, semantically coherent outputs and highlights entropy regions where LLM decisions are likely to be unreliable, offering practical guidelines for when human oversight is warranted. We evaluate the robustness of our scheme under different generation lengths and quantization levels, dimensions overlooked in prior studies, demonstrating that our approach remains reliable even in resource constrained deployments. A total of 116 experiments on TriviaQA, NQ, SVAMP, and SQuAD across multiple architectures including Mistral-7B, Mistral-7B-instruct, Falcon-rw-1b, LLaMA-3.2-1b, LLaMA-2-13b-chat, LLaMA-2-7b-chat, LLaMA-2-13b, and LLaMA-2-7b show consistent improvements in AUROC and AURAC over state of the art baselines.
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