ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices

• URL: http://arxiv.org/abs/2503.18242v2
• Date: Sat, 14 Jun 2025 19:57:53 GMT
• Title: ShED-HD: A Shannon Entropy Distribution Framework for Lightweight Hallucination Detection on Edge Devices
• Abstract summary: Large Language Models (Ms.LS) have demonstrated impressive capabilities on a broad array of tasks, but their tendency to produce hallucinations.<n>We propose a novel hallucination detection framework that efficiently detects distinctive uncertainty patterns.
• Score: 2.193668764461763
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities on a broad array of NLP tasks, but their tendency to produce hallucinations$\unicode{x2013}$plausible-sounding but factually incorrect content$\unicode{x2013}$poses severe challenges in high-stakes domains. Existing hallucination detection methods either bear the computational cost of multiple inference passes or sacrifice accuracy for efficiency with single-pass approaches, neither of which is ideal in resource-constrained environments such as edge devices. We propose the Shannon Entropy Distribution Hallucination Detector (ShED-HD), a novel hallucination detection framework that bridges this gap by classifying sequence-level entropy patterns using a lightweight BiLSTM architecture with single-headed attention. In contrast to prior approaches, ShED-HD efficiently detects distinctive uncertainty patterns across entire output sequences, preserving contextual awareness. Through in-depth evaluation on three datasets (BioASQ, TriviaQA, and Jeopardy Questions), we show that ShED-HD significantly outperforms other computationally efficient approaches in the out-of-distribution setting, while achieving comparable performance in the in-distribution setting. ShED-HD facilitates hallucination detection that is low-cost, accurate, and generalizable, improving the credibility of content generated by LLMs in resource-constrained environments where trustworthy AI functionality is crucial.

Related papers

• TRE: Training-Free Hallucination Detection for Diffusion Language Models [9.622962795441852]
  We propose TRE, a training-free hallucination detection metric for D-LLMs.<n> TRE estimates hallucination risk directly from the entropy signals of a single generation, without requiring any detector training or repeated sampling.<n>Extensive experiments on multiple D-LLMs and QA datasets demonstrate that TRE achieves competitive performance, while enjoying strong generalizability, efficiency, and robustness.
  arXiv  Detail & Related papers  (2026-06-29T00:47:48Z)
• Entropy Distribution as a Fingerprint for Hallucinations in Generative Models [1.5135066879411019]
  Calibrated Entropy Score (CES) is a lightweight algorithm for hallucination detection.<n>CES combines the mean signal with the maximum signal of the generated entropy through a calibrated reference CDF.<n>CES is statistically indistinguishable from multi-sample methods that require far greater computational cost.
  arXiv  Detail & Related papers  (2026-05-27T10:12:44Z)
• Scalable Token-Level Hallucination Detection in Large Language Models [63.3426544914783]
  Internal hallucinations are difficult to detect in reasoning-intensive tasks.<n>We propose TokenHD, a holistic pipeline for training token-level hallucination detectors.
  arXiv  Detail & Related papers  (2026-05-12T16:47:40Z)
• Locate-then-Sparsify: Attribution Guided Sparse Strategy for Visual Hallucination Mitigation [68.41785694664011]
  We propose a plug-and-play framework called Locate-Then-Sparsify for Feature Steering (LTS-FS)<n>It controls the steering intensity according to the hallucination relevance of each layer.<n>Our framework effectively mitigates hallucination while preserving strong performance.
  arXiv  Detail & Related papers  (2026-03-17T09:16:50Z)
• FaithSCAN: Model-Driven Single-Pass Hallucination Detection for Faithful Visual Question Answering [14.550872089352943]
  FaithSCAN is a lightweight network that detects hallucinations by exploiting rich internal signals of vision-language models.<n>We extend the LLM-as-a-Judge paradigm to VQA hallucination and propose a low-cost strategy to automatically generate model-dependent supervision signals.<n>In-depth analysis shows hallucinations arise from systematic internal state variations in visual perception, cross-modal reasoning, and language decoding.
  arXiv  Detail & Related papers  (2026-01-01T09:19:39Z)
• Neural Probe-Based Hallucination Detection for Large Language Models [4.211691393530721]
  Large language models excel at text generation and knowledge question-answering tasks.<n>They are prone to generating hallucinated content, severely limiting their application in high-risk domains.<n>We propose a neural network-based framework for token-level hallucination detection.
  arXiv  Detail & Related papers  (2025-12-24T05:10:19Z)
• VOGUE: Guiding Exploration with Visual Uncertainty Improves Multimodal Reasoning [62.09195763860549]
  Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models (LLMs) but struggles with exploration.<n>We introduce $textbfVOGUE (Visual Uncertainty Guided Exploration)$, a novel method that shifts exploration from the output (text) to the input (visual) space.<n>Our work shows that grounding exploration in the inherent uncertainty of visual inputs is an effective strategy for improving multimodal reasoning.
  arXiv  Detail & Related papers  (2025-10-01T20:32:08Z)
• Semantic Energy: Detecting LLM Hallucination Beyond Entropy [106.92072182161712]
  Large Language Models (LLMs) are being increasingly deployed in real-world applications, but they remain susceptible to hallucinations.<n>Uncertainty estimation is a feasible approach to detect such hallucinations.<n>We introduce Semantic Energy, a novel uncertainty estimation framework.
  arXiv  Detail & Related papers  (2025-08-20T07:33:50Z)
• ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMs [50.18087419133284]
  hallucination detection methods leveraging hidden states predominantly focus on static and isolated representations.<n>We introduce a novel metric, the ICR Score, which quantifies the contribution of modules to the hidden states' update.<n>We propose a hallucination detection method, the ICR Probe, which captures the cross-layer evolution of hidden states.
  arXiv  Detail & Related papers  (2025-07-22T11:44:26Z)
• HIDE and Seek: Detecting Hallucinations in Language Models via Decoupled Representations [17.673293240849787]
  Contemporary Language Models (LMs) often generate content that is factually incorrect or unfaithful to the input context.<n>We propose a single-pass, training-free approach for effective Hallucination detectIon via Decoupled rEpresentations (HIDE)<n>Our results demonstrate that HIDE outperforms other single-pass methods in almost all settings.
  arXiv  Detail & Related papers  (2025-06-21T16:02:49Z)
• Preemptive Hallucination Reduction: An Input-Level Approach for Multimodal Language Model [1.124958340749622]
  This study presents a novel ensemble-based preprocessing framework that adaptively selects the most appropriate filtering approach.<n>The method achieves a 44.3% reduction in hallucination rates, as measured by Natural Language Inference (NLI) scores.<n>The findings highlight the importance of adaptive preprocessing techniques in mitigating hallucinations, paving the way for more reliable multimodal systems.
  arXiv  Detail & Related papers  (2025-05-29T21:09:34Z)
• HAODiff: Human-Aware One-Step Diffusion via Dual-Prompt Guidance [71.5820853722963]
  Human-centered images often suffer from severe generic degradation during transmission and are prone to human motion blur (HMB)<n>We design a degradation pipeline that simulates the coexistence of HMB and generic noise, generating synthetic degraded data to train our proposed HAODiff.<n>For fair evaluation, we introduce MPII-Test, a benchmark rich in combined noise and HMB cases.
  arXiv  Detail & Related papers  (2025-05-26T09:24:11Z)
• Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation [78.78421340836915]
  We systematically investigate reference-free hallucination detection in open-domain long-form responses.<n>Our findings reveal that internal states are insufficient for reliably distinguishing between factual and hallucinated content.<n>We introduce a new paradigm, named RATE-FT, that augments fine-tuning with an auxiliary task for the model to jointly learn with the main task of hallucination detection.
  arXiv  Detail & Related papers  (2025-05-18T07:10:03Z)
• Hallucination Detection in LLMs via Topological Divergence on Attention Graphs [64.74977204942199]
  Hallucination, i.e., generating factually incorrect content, remains a critical challenge for large language models. We introduce TOHA, a TOpology-based HAllucination detector in the RAG setting.
  arXiv  Detail & Related papers  (2025-04-14T10:06:27Z)
• SINdex: Semantic INconsistency Index for Hallucination Detection in LLMs [2.805517909463769]
  Large language models (LLMs) are increasingly deployed across diverse domains, yet they are prone to generating factually incorrect outputs.<n>We introduce a novel and scalable uncertainty-based semantic clustering framework for automated hallucination detection.
  arXiv  Detail & Related papers  (2025-03-07T23:25:19Z)
• HalluCounter: Reference-free LLM Hallucination Detection in the Wild! [6.5037356041929675]
  HalluCounter is a reference-free hallucination detection method that utilizes both response-response and query-response consistency and alignment patterns.<n>Our method outperforms state-of-the-art approaches by a significant margin, achieving over 90% average confidence in hallucination detection across datasets.
  arXiv  Detail & Related papers  (2025-03-06T16:59:18Z)
• CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base [29.477973983931083]
  We propose CutPaste&Find, a lightweight and training-free framework for detecting hallucinations in LVLM-generated outputs.<n>At the core of our framework is a Visual-aid Knowledge Base that encodes rich entity-attribute relationships and associated image representations.<n>We introduce a scaling factor to refine similarity scores, mitigating the issue of suboptimal alignment values even for ground-truth image-text pairs.
  arXiv  Detail & Related papers  (2025-02-18T07:06:36Z)
• HuDEx: Integrating Hallucination Detection and Explainability for Enhancing the Reliability of LLM responses [0.12499537119440242]
  This paper proposes an explanation enhanced hallucination-detection model, coined as HuDEx.<n>The proposed model provides a novel approach to integrate detection with explanations, and enable both users and the LLM itself to understand and reduce errors.
  arXiv  Detail & Related papers  (2025-02-12T04:17:02Z)
• Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs [32.901839335074676]
  Hallucinations present a major challenge to the practical adoption of Large Language Models. Recent work by Farquhar et al. (2024) proposes semantic entropy (SE), which can detect hallucinations by estimating uncertainty in the space semantic meaning for a set of model generations. We propose SEPs, which directly approximate SE from the hidden states of a single generation.
  arXiv  Detail & Related papers  (2024-06-22T19:46:06Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus [99.33091772494751]
  Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. LLMs are prone to hallucinate untruthful or nonsensical outputs that fail to meet user expectations. We propose a novel reference-free, uncertainty-based method for detecting hallucinations in LLMs.
  arXiv  Detail & Related papers  (2023-11-22T08:39:17Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)
• Detecting Hallucinated Content in Conditional Neural Sequence Generation [165.68948078624499]
  We propose a task to predict whether each token in the output sequence is hallucinated (not contained in the input) We also introduce a method for learning to detect hallucinations using pretrained language models fine tuned on synthetic data.
  arXiv  Detail & Related papers  (2020-11-05T00:18:53Z)
• SADet: Learning An Efficient and Accurate Pedestrian Detector [68.66857832440897]
  This paper proposes a series of systematic optimization strategies for the detection pipeline of one-stage detector. It forms a single shot anchor-based detector (SADet) for efficient and accurate pedestrian detection. Though structurally simple, it presents state-of-the-art result and real-time speed of $20$ FPS for VGA-resolution images.
  arXiv  Detail & Related papers  (2020-07-26T12:32:38Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.