Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 21 references
Abstract
Large Language Models frequently generate responses that appear fluent and coherent but may contain factually incorrect information, a phenomenon known as hallucinations. Hallucinations have emerged as a significant barrier to the effective application of Large Language Models. Some studies suggest that hallucinations can be detected by analyzing structural properties of attention maps. However, such methods primarily focus on attention structures and their spectral features, whereas uncertainty, randomness, and other statistical measures can also be leveraged to detect hallucinations. In this work, we propose a method for detecting hallucinations using spectral and statistical features extracted from attention maps. We compute eigenvalues of the Laplacian matrix from each attention map to capture spectral features and compute statistical measures for every attention head. The spectral and statistical features are used as a feature vector for hallucination detection. We conducted experiments on the HaluEval dataset across multiple language models, and the results show that jointly modeling structures and uncertainty-aware characteristics leads to more robust hallucination detection.
LLMs have become more widespread in many critical areas, which has increased the risk of hallucinations, or instances where generated outputs contain false or misleading information. To address this problem, this paper introduces HalluciNet v3.0+, which is an adaptive multi-LLM hallucination detection framework that employs advanced preprocessing, feature engineering, and classification methods for strong factual verification. In the preprocessing stage, the system begins with normalizing raw LLM-generated outputs; using linguistic parsing through spaCy to break up the LLM outputs into structured claims; and then using Transformer-based tokenization for contextual refinement of the structured claims. For the feature extraction step, the system uses both BERT and BGE embeddings to create dense semantic representations of the structured claims and the context and relationship dependencies between the structured claims and the retrieved evidence. A hybrid feature selection is achieved by combining Recursive Feature Elimination (RFE) and attention-weight optimization methods, which provides for the selection of the most discriminative features while minimizing dimensionality. A novel ensemble-based classification solution is implemented using DeBERTa-v3 for Natural Language Inference, XGBoost for structured decision learning, and consensus scoring across multiple LLM sources to produce a final predicted output. A two-step RAG (Retrieval-Augmented Generation) module optimizes retrieval and retrieval based on the quality of the retrieved item in a cross-encoder-based framework while preserving the inherent factual quality of the item's contents. This system is designed to be fully scalable and production-ready and is therefore expected to provide trustworthy enhancements to the use of LLM-driven products.
S.Rajeshwari, M. K, Mohamed Habith S et al.· International Conference Com...· 0 citations
Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence, so a fully black-box framework that models hallucination as a structured uncertainty pattern is proposed.
Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia et al.· 0 citations
This work introduces the task of hallucination span detection with input-side evidence alignment, which jointly identifies hallucinated spans and aligns output tokens with the corresponding input evidence.
This work proposes M ulti-Model C ontrastive D ecoding (MCD), which integrates a pretrained language model with an evil model and a truthful model for contrastive decoding and effectively reduces hallucinations in LLMs and outperforms state-of-the-art methods across various benchmarks.
Chenyu Zhu, Yefeng Liu, Hao Zhang et al.· Neural Information Processin...· 7 citations
A lightweight linear detector is built on top of Role-Break that requires no fine-tuning of the VLM, whose feature dimension stays below 5,000 and reaches an average AUROC of 93.23 across six VLMs and four benchmarks.
Mingyu Wang, Weilin Jin, Wenbo Li et al.· 0 citations
TRE is a parameter-free and single-run metric that estimates hallucination risk directly from the entropy signals of a single generation, without requiring any detector training or repeated sampling, and enjoys strong generalizability, efficiency, and robustness.
Pengcheng Weng, Y. Qian, Yue Tan et al.· 0 citations