English(EN)The Cost of Knowing: A Resource-Aware Protocol for Benchmarking Hallucination Beyond Static Leaderboards
新研究着眼于法律、多模态和通用文本生成中的大型语言模型幻觉问题
作者PulseAugur 编辑部·[33 个来源]·
多篇在arXiv上发表的研究论文探讨了检测和减轻大型语言模型（LLM）幻觉的方法。一项研究对法律幻觉检测进行了基准测试，发现虽然GPT-5等较新模型有所改进，但它们在细微的错误类别上仍有困难，并且需要资源密集型的验证。另一篇论文介绍了HallDetect，一个用于各种生成任务的无参考幻觉检测框架。其他研究侧重于引发内在幻觉的对抗性攻击、神经多样性在减少幻觉中的作用，以及大型视觉语言模型中的特定模式。此外，还提出了一个新的基准KnowHal，用于全面的多模态幻觉评估，并提出了一个包含人类书写样本的数据集，用于细粒度的视觉和语言幻觉基准测试。
AI
arXiv:2608.10430v1 Announce Type: cross Abstract: Large Language Models (LLMs) deployed as AI agents frequently exhibit user specification-grounding failures, executing hallucinated, undesired actions to force a resolution rather than expressing uncertainty. Existing detection me…
arXiv cs.AI
TIER_1English(EN)·Zichuan Wang, Songlin Yang, Bo Peng, Zhenchen Tang, Yang Li, Beibei Dong, Jing Dong·
arXiv:2608.07302v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely attributes this to insufficient visual attention. However, we find that both real a…
arXiv cs.CL
TIER_1English(EN)·Achir Oukelmoun, Nasredine Semmar, Ga\"el De Chalendar·
arXiv:2608.05823v1 Announce Type: new Abstract: The reliability of Large Language Models (LLMs) is often compromised by factual inconsistencies, including hallucinations---cases where generated content is not supported by the underlying source. We present HallDetect, a lightweigh…
arXiv cs.CL
TIER_1English(EN)·Patty Liu, Dominik Stammbach, Peter Henderson·
arXiv:2606.21155v2 Announce Type: replace Abstract: Attorneys, judges, and pro se filers increasingly use AI to draft legal documents, yet these tools frequently fabricate citations. Despite predictions that newer models would hallucinate less or that court sanctions would deter …
arXiv:2510.20690v3 Announce Type: replace-cross Abstract: Language models continue to hallucinate despite increases in parameters, compute, and data. We propose neural diversity -- decorrelated parallel representations -- as a principled mechanism that reduces hallucination rates…
arXiv cs.CL
TIER_1English(EN)·Atri Vivek Sharma, Brian Formento, Alessio Lomuscio·
arXiv:2608.04286v1 Announce Type: new Abstract: Large language models (LLMs) are often used in conjunction with external knowledge sources to improve their factual accuracy and decrease hallucinations, through methods such as Retrieval-Augmented Generation (RAG). However, these s…
arXiv cs.AI
TIER_1English(EN)·Amir Mohammad Ezzati, Kiyan Rezaee, Bardiya Kariminia, Mohamad Amin Yousefi, Asal Mohammadjafari Mamaqani, Behrad Samimi, Mohammad Hossein Rohban·
arXiv:2608.03817v1 Announce Type: cross Abstract: Large vision--language models (LVLMs) demonstrate strong multimodal reasoning capabilities but remain prone to hallucination, where model predictions are not grounded in visual evidence. Existing black-box hallucination detection …
arXiv:2608.03966v1 Announce Type: new Abstract: Large language models can generate fluent Arabic answers while introducing factual errors that are difficult to identify and verify. Existing Arabic hallucination resources often assign a binary label to an entire response, indicati…
arXiv:2608.03782v1 Announce Type: new Abstract: Hallucination remains a critical challenge for developing trustworthy Multimodal Large Language Models (MLLMs). While existing benchmarks mainly focus on entity, attribute, and relation hallucinations, knowledge-related failures are…
arXiv cs.CL
TIER_1English(EN)·Timothee Mickus, Claudio Savelli, Eduardo Cal\`o, Emilio Raimond, Stella Frank, Hengyu Luo, Flavio Giobergia, Vincent Segonne, Chuyuan Li, Aman Sinha, Lorenzo Vaiani, J\"org Tiedemann, Ra\'ul V\'azquez·
arXiv:2608.01021v1 Announce Type: cross Abstract: In an age of rapid model turnover, how do we make hallucination evaluation more perennial? We explore whether human-written hallucination samples could take the place of model-generated hallucinations, in order to make benchmarkin…
arXiv cs.CL
TIER_1English(EN)·Jakub Binkowski, Kamil Adamczewski, Tomasz Kajdanowicz·
arXiv:2604.10697v2 Announce Type: replace Abstract: Large language models frequently exhibit hallucinations: fluent and confident outputs that are factually incorrect or unsupported by the input context. While recent hallucination detection methods have explored various features …
arXiv cs.LG
TIER_1English(EN)·Chenlin Liu, Minghui Fang, Zhonghao Bi, Zekai Su, Rong Wang, Jiqing Han·
arXiv:2608.00722v1 Announce Type: cross Abstract: Language model-based text-to-speech (LM-based TTS) remains vulnerable to speech hallucinations that deviate from the target text. Existing mitigation mainly relies on architectural changes or additional training, while decoding-ti…
arXiv:2607.29240v1 Announce Type: cross Abstract: In vision--language models, commonsense-driven hallucination (CDH) occurs when a model's commonsense prior overrides clear visual evidence of an atypical state. For example, a model may report that a visibly six-fingered hand has …
arXiv:2502.20780v2 Announce Type: replace-cross Abstract: The increasing use of vision-language models (VLMs) in healthcare applications presents great challenges related to hallucinations, in which the models may generate seemingly plausible results that are in fact incorrect. S…
arXiv cs.CL
TIER_1English(EN)·Mohammad Baqar, Rajat Khanda·
arXiv:2502.10497v2 Announce Type: replace Abstract: Recent advancements in Generative AI have significantly improved the efficiency and adaptability of natural language processing (NLP) systems, particularly through Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA)…
arXiv:2506.14766v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) frequently hallucinate by over-committing to spurious visual cues. Prior remedies-Visual and Instruction Contrastive Decoding (VCD, ICD)-mitigate this issue, yet the mechanism remai…
arXiv cs.CL
TIER_1English(EN)·Yubo Sun, Chunyi Peng, Yukun Yan, Shi Yu, Zhenghao Liu, Sen Mei, Chi Chen, Maosong Sun·
arXiv:2510.09733v2 Announce Type: replace Abstract: Visual Retrieval-Augmented Generation (VRAG) has emerged as a promising paradigm for equipping Vision-Language Models (VLMs) with external visual evidence, enabling them to go beyond parametric knowledge when answering visually …
arXiv:2607.22661v1 Announce Type: new Abstract: Diffusion large language models (D-LLMs) have recently gained increasing attention, yet their reliability is significantly hindered by the hallucination problem. Existing hallucination detection approaches for D-LLMs mainly follow a…
arXiv cs.CL
TIER_1English(EN)·Debmalya Panigrahi, Fan Wei, Ian Zhang·
arXiv:2607.23361v1 Announce Type: cross Abstract: Language generation in the limit is an elegant model introduced by Kleinberg and Mullainathan [KM24] to formally study language generation by an algorithm that learns solely based on example strings. In this model, an algorithm is…
arXiv:2607.24586v1 Announce Type: cross Abstract: Large Language Models can produce fluent text that is false, unsupported by the available evidence, or inconsistent with information that appears to be internally represented by the model. We study hallucination detection from the…
arXiv cs.LG
TIER_1English(EN)·Fabrizio Frasca, Guy Bar-Shalom, Yftah Ziser, Haggai Maron·
arXiv:2509.24770v2 Announce Type: replace Abstract: Large Language Models (LLMs) often generate incorrect or unsupported content, known as hallucinations. Existing detection methods rely on heuristics or simple models over isolated computational traces such as activations, or att…
On standard factuality tasks, frontier models now cluster near the top of the scale. The question is therefore shifting from how factual a system is toward how much compute that factuality costs. Static leaderboards score factuality in isolation and treat compute as free, so they…
arXiv cs.LG
TIER_1English(EN)·Junlin Fang, Do Nguyen-Thanh, Xiaogang Xu, Zhen Fang, Sean Du·
arXiv:2607.22098v1 Announce Type: cross Abstract: Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing them is non-trivial because long trajectories often…
Large reasoning models (LRMs) generate long reasoning traces before producing final answers. While these traces may contain useful signals for hallucination detection, harnessing them is non-trivial because long trajectories often include noisy steps that obscure the cues relevan…
arXiv cs.CV
TIER_1English(EN)·Joanna Wojciechowicz, Maria {\L}ubniewska, Jakub Antczak, Justyna Baczy\'nska, Wojciech Gromski, Wojciech Koz{\l}owski, Maciej Zieba·
arXiv:2602.14633v2 Announce Type: replace Abstract: We introduce VIGIL (Visual Inconsistency & Generative In-context Lucidity), a benchmark dataset and framework that provides a fine-grained categorization of hallucinations in the multimodal image recontextualization task for lar…
arXiv:2608.05616v1 Announce Type: new Abstract: Despite the remarkable progress of large vision language models (LVLMs), object hallucination remains a fundamental challenge that hinders their trustworthy deployment. A key finding motivates our work: real and hallucinated object …
arXiv:2607.29412v1 Announce Type: new Abstract: Despite remarkable progress in vision-language generation, Vision-Language Models (VLMs) remain prone to hallucinations, producing content that is inconsistent with or unsupported by the input image. Existing works largely design de…
arXiv:2607.27823v1 Announce Type: new Abstract: Large vision-language models (LVLMs) often hallucinate objects that are absent from an image. Despite recent progress, existing mitigation methods still lack reliable object-level grounding diagnostics and therefore tend to apply co…
<div class="medium-feed-item"><p class="medium-feed-snippet">Hallucination is a strange word to use for an error in the mathematical processes running inside a computer.</p><p class="medium-feed-link"><a href="https://medium.com/@robinjacob.arts/hallucination-why-ai-models-have-t…
<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1vh3ani/knowledge_vs_hallucination_rate_what_is_your/"> <img alt="Knowledge vs. hallucination rate: what is your favorite model?" src="https://preview.redd.it/h7dwy09z2rhh1.png?width=140&height=140&cro…
<p>You ask an AI a question. It answers in fluent, confident prose — complete with a study, a percentage, and a name. Some of it is wrong, and nothing about the wording tells you which part. That's the whole problem with hallucinations: the errors wear the same suit as the facts.…
