English(EN)Temporal Multi-Signal Fusion for Token-Level Hallucination Detection
新研究通过新颖的检测方法解决LLM和VLM的幻觉问题 · 已追踪7个来源
作者PulseAugur 编辑部·[13 个来源]·
研究人员正在开发新的方法来对抗大型语言模型（LLM）和视觉语言模型（VLM）中的幻觉。一种名为InnerExpert的方法，利用来自混合专家（MoE）架构的内部信号进行每令牌幻觉检测。另一个系统AutoResearch，旨在通过整合创意生成与可靠的实验来为自主研究奠定基础。其他方法侧重于通过将输出与输入证据对齐或跨模型层聚合真实性信号来检测幻觉跨度。对于医学VLM，一个名为CAST的框架在推理过程中使用反事实解剖学指导进行无标注幻觉缓解。
AI
arXiv:2608.19075v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) often hallucinate, generating content that the input image does not support. Preventing such content during decoding calls for a candidate-specific measure of how strongly the image supports th…
arXiv cs.CL
TIER_1English(EN)·Ruizhi Zhang, Jinwei Chen, Xiangju Lu, He Yan, Mo Yu, Junmin Zhu, Wei Zhang·
arXiv:2608.18082v1 Announce Type: new Abstract: Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge. Long novels are better suited than news or papers for researching these hallucinations, due to th…
arXiv:2608.18115v1 Announce Type: cross Abstract: Token-level hallucination detectors score each token independently from a single signal, and fail exactly when the generating model is confidently wrong. This paper instead treats hallucination as a temporally extended span and de…
arXiv cs.AI
TIER_1English(EN)·Joao Fonseca, Rodrigo Rodrigues, Paolo Romano·
arXiv:2608.17687v1 Announce Type: new Abstract: Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known as hallucinations. Most existing detection methods operate at the answer or sen…
arXiv:2608.17906v1 Announce Type: new Abstract: Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically grounded. We introduce AutoResearch, a two-stage syste…
Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically grounded. We introduce AutoResearch, a two-stage system that connects Idea Generation with Idea Execut…
Despite their widespread use, Large Language Models (LLMs) remain limited by a fundamental problem: the generation of plausible but false content, known as hallucinations. Most existing detection methods operate at the answer or sentence level, yet per-token detection is essentia…
arXiv:2608.15804v1 Announce Type: new Abstract: Hallucinations remain a major obstacle to the reliable use of large language models (LLMs) in conditional text generation. Existing methods primarily assess the factuality of an entire generated text, providing limited insight into …
arXiv:2608.16353v1 Announce Type: cross Abstract: Even well-aligned large language models confidently generate factually incorrect text, making hallucination a persistent reliability risk in high-stakes deployments. These models nonetheless carry linearly separable truthfulness s…
UniProbe is a lightweight learnable detector that uses a directed graph and alternating GNN, ViT, and GRU modules to identify hallucinated tokens in frozen large vision-language models, enabling real-time resampling during generation.
Hallucination is detected as temporally extended spans via sequence labeling over fused external features, achieving robust cross-model performance without internal model access.
arXiv:2506.01367v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly integrated into agentic AI systems, yet their propensity to generate hallucinations remains a critical safety concern. Detecting these factual errors at test-time, particularly…
arXiv:2608.17427v1 Announce Type: new Abstract: Medical vision-language models (Med-VLMs) have demonstrated strong performance on medical visual question answering, yet they remain prone to hallucination, generating clinically unsupported statements that are insufficiently ground…
