hallucination-detection

This paper identifies and characterizes Decomposition-Induced Context-Memory Conflict (DI-CC), a failure mode in decompose-then-verify pipelines where decomposition substitutes the model's parametric beliefs for the source text. The authors show it is mechanistically related to classical context-memory conflict, that SelfCheckGPT fails to detect it, and that context-aware decoding suppresses it but introduces severe parsing failures.

This paper introduces the Latent Critic, a lightweight LoRA adapter that detects hallucinated agent actions in real time by restructuring the transformer's residual stream into localized natural-language feedback, achieving 0.966 AUROC and enabling self-correction.

This paper introduces Prompt Embedding Probes (PEP), a parameter-efficient extension of linear probes that uses learnable prompt embeddings on hidden states to detect hallucinations in frozen LLMs. Evaluations on TriviaQA, GSM8K, and MedQA with Qwen3 models show improvements over standard linear probes, including in pre-generation and cross-model settings.

This paper presents HallDetect, a lightweight and reference-free framework for hallucination detection that decomposes generated content into atomic claims and verifies them with a compact entailment model. It outperforms comparably resourced baselines on multiple benchmarks and provides a claim-to-span audit trail.

This paper introduces TriQua, a framework for LLM factuality evaluation that adaptively represents facts as triples or hyperrelational facts with contextual qualifiers, along with TriQuaScore for fine-grained factuality scoring. It demonstrates strong alignment with human annotations and improved evidence-based verification over existing methods.

A pre-registered study proposes a first-token hallucination detection method using internal model signals across 10 models, finding no universal detector but a universal above-chance floor, with public code and verification scripts.

Introduces ReDe, a framework that denoises reasoning traces by filtering irrelevant and repetitive steps to improve hallucination detection in large reasoning models, achieving up to 87.32 AUROC on TruthfulQA.

The paper proposes a neurosymbolic framework that lifts LLM outputs into typed compositional derivations using Combinatory Categorial Grammar, enabling structural checking and early hallucination detection.

This paper proposes Logical Graph Uncertainty (LGU), a framework that models implication and incompatibility among answers to improve uncertainty estimation in LLMs, outperforming semantic entropy baselines by up to 7.1% AUROC and 3.5% AUARC across benchmarks.

This paper proposes diversity-oriented fine-tuning strategies to improve uncertainty-based hallucination detection in LLMs by encouraging varied generations, making hallucinations more detectable via semantic entropy.

ASK-NN is an asymmetric nearest-neighbor test for detecting distribution drifts between reference and query samples, with applications to LLM hallucination detection and artificial-text detection. It is computationally efficient, has theoretical guarantees, and performs competitively against baselines on synthetic and real-world benchmarks.

An open-source tool for training LLMs on consumer hardware, featuring real-time neural visualization for hallucination detection and model introspection, currently supporting small-scale models.

This paper introduces diversion decoding, a novel method for detecting hallucinations in large language models by actively challenging model responses during the decoding phase to extract features for training an uncertainty heuristic, achieving superior performance with lower computational complexity.

This paper introduces a benchmark framework to evaluate the faithfulness of LLM-generated clinical trial summaries for different stakeholder audiences, tests GPT-4o, Claude Sonnet 4.6, and Gemini 2.5 Flash on 1,800 summaries, and proposes a knowledge-graph-augmented retrieval system that improves faithfulness scores.

This study evaluates whether J-space entropy (inspired by Anthropic's Jacobian Lens) can serve as an error predictor across seven datasets on Qwen3-4B. Results show it can complement output confidence for factual retrieval but is not a general hallucination detector, with strong task dependence.

This article evaluates Anthropic's J-Space hallucination detection method across 7 datasets on Qwen3-4B, finding it effective for catching high-confidence errors in factual retrieval but blind to internalized myths and failing on math tasks where thresholds don't transfer.

This paper empirically characterizes uncertainty in thinking-mode visual language models, demonstrating that the thinking chain entropy is a more reliable signal for hallucination detection than conventional answer token distribution, which collapses in these models.

Introduces Hallucination Self-Play (HSP), a framework that bootstraps a detector using an evolved generator via reinforcement learning, enabling small LLMs to match advanced LLMs on faithfulness hallucination detection without external supervision.

This paper investigates whether activation patterns in Polish Bielik LLMs can detect entity familiarity before generation, using unsupervised dispersion measures that achieve near-perfect separation of known vs. fabricated entities across model scales, while showing that factual reliability improves sharply with scale but is harder to predict from activations.

The author tested Anthropic's Jacobian Lens on open models, then it evolved into a local-model hallucination router for detecting AI hallucinations.