arXiv:2606.19404v1 Announce Type: cross Abstract: Hallucination detection in large language models (LLMs) is deployment-critical, and recent work shows that the spectrum of attention-derived graph Laplacians carries strong signal about reasoning quality. Prior spectral diagnostic…
arXiv:2505.20045v3 Announce Type: replace Abstract: While large language models (LLMs) have become highly capable, they remain prone to factual inaccuracies, commonly referred to as "hallucinations." Uncertainty quantification (UQ) offers a promising way to mitigate this issue, b…
arXiv:2606.15782v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-language understanding and natural-language response generation. However, these systems can still produce overconfident predictions and halluci…
arXiv:2606.14758v1 Announce Type: cross Abstract: As Vision-Language Models are increasingly deployed in safety-critical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vision-Language Models often suffer from semantic hal…
arXiv cs.AI
TIER_1English(EN)·Han Sun, Qin Li, Peixin Wang, Min Zhang·
arXiv:2603.24058v2 Announce Type: replace-cross Abstract: Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barrier to their deployment in high-stakes scenarios such as autonomous driv…
Token-level hallucination detection is reformulated as a quickest change detection problem, revealing fundamental limits on detection delay and demonstrating superior performance through causal recurrent modeling.
arXiv cs.CV
TIER_1English(EN)·Karn Tiwari, Varnith Chordia, Prathosh A P·
arXiv:2606.20419v1 Announce Type: new Abstract: Vision-language models (VLMs) often generate fluent but visually unsupported descriptions, especially by mentioning objects absent from the image. We propose QK Product Steering, a data-free, training-free, and zero-inference-cost w…
Vision-language models (VLMs) often generate fluent but visually unsupported descriptions, especially by mentioning objects absent from the image. We propose QK Product Steering, a data-free, training-free, and zero-inference-cost weight edit for reducing object hallucination. Th…
Vision-Language models (VLMs) reliability in medical diagnosis is challenged by trust-undermining hallucinations. Existing hallucination detection approaches mainly focus on identifying factual inconsistencies between generated text and reference data. While some studies analyze …
Medium — fine-tuning tag
TIER_1English(EN)·Shreyas Vidyarthi·
🤖 New VLM Framework Reduces Hallucinations with Evidence-Grounded Reasoning The CaVe VLM CoT framework reduces hallucinations in Vision Language Models by enforcing evidence grounded reasoning through a five stage closed loop pipeline. This development comes as researchers contin…
