Papers Hallucination
“Hallucination” 태그가 달린 논문 1,816편 · 필터 해제
Mitigating Object Hallucinations via Sentence-Level Early Intervention
Multimodal large language models (MLLMs) have revolutionized cross-modal understanding but continue to struggle with hallucinations - fabricated content contradicting visual inputs. Existing hallucination mitigation meth…
HallucinationMM-VetSentenceText Generation+1ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP), which i…
Depth EstimationHallucinationLanguage ModelingLanguage Modelling+2UQLM: A Python Package for Uncertainty Quantification in Large Language Models
Hallucinations, defined as instances where Large Language Models (LLMs) generate false or misleading content, pose a significant challenge that impacts the safety and trust of downstream applications. We introduce UQLM, …
HallucinationUncertainty QuantificationDeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning
Retrosynthesis, the identification of precursor molecules for a target compound, is pivotal for synthesizing complex molecules, but faces challenges in discovering novel pathways beyond predefined templates. Recent large…
HallucinationLarge Language ModelRetrosynthesisReLoop: "Seeing Twice and Thinking Backwards" via Closed-loop Training to Mitigate Hallucinations in Multimodal understanding
While Multimodal Large Language Models (MLLMs) have achieved remarkable progress in open-ended visual question answering, they remain vulnerable to hallucinations. These are outputs that contradict or misrepresent input …
HallucinationQuestion AnsweringVisual Question AnsweringThe Future is Agentic: Definitions, Perspectives, and Open Challenges of Multi-Agent Recommender Systems
Large language models (LLMs) are rapidly evolving from passive engines of text generation into agentic entities that can plan, remember, invoke external tools, and co-operate with one another. This perspective paper inve…
Explanation GenerationHallucinationRecommendation SystemsText Generation+1GAF-Guard: An Agentic Framework for Risk Management and Governance in Large Language Models
As Large Language Models (LLMs) continue to be increasingly applied across various domains, their widespread adoption necessitates rigorous monitoring to prevent unintended negative consequences and ensure robustness. Fu…
HallucinationManagementMitigating Hallucination of Large Vision-Language Models via Dynamic Logits Calibration
Large Vision-Language Models (LVLMs) have demonstrated significant advancements in multimodal understanding, yet they are frequently hampered by hallucination-the generation of text that contradicts visual input. Existin…
HallucinationText GenerationHalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation
Recent progress in vision-language segmentation has significantly advanced grounded visual understanding. However, these models often exhibit hallucinations by producing segmentation masks for objects not grounded in the…
counterfactualCounterfactual ReasoningHallucinationHallucination Evaluation+4Feature Hallucination for Self-supervised Action Recognition
Understanding human actions in videos requires more than raw pixel analysis; it relies on high-level semantic reasoning and effective integration of multimodal features. We propose a deep translational action recognition…
Action RecognitionHallucinationobject-detectionObject Detection+3Seeing is Believing? Mitigating OCR Hallucinations in Multimodal Large Language Models
Recent advancements in multimodal large language models have enhanced document understanding by integrating textual and visual information. However, existing models exhibit incompleteness within their paradigm in real-wo…
document understandingHallucinationOptical Character Recognition (OCR)KnowRL: Exploring Knowledgeable Reinforcement Learning for Factuality
Large Language Models (LLMs), particularly slow-thinking models, often exhibit severe hallucination, outputting incorrect content due to an inability to accurately recognize knowledge boundaries during reasoning. While R…
HallucinationHallucination Evaluationreinforcement-learningReinforcement Learning+1Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as…
HallucinationImitation LearningRobot ManipulationHEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models
Large language models (LLMs) are increasingly being adopted as the cognitive core of embodied agents. However, inherited hallucinations, which stem from failures to ground user instructions in the observed physical envir…
HallucinationDiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
Vision-Language Models (VLMs) now generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers originally designed for single-sentence caption-to-graph mapping. Current approaches ty…
Graph GenerationHallucinationSentenceASCD: Attention-Steerable Contrastive Decoding for Reducing Hallucination in MLLM
Multimodal Large Language Model (MLLM) often suffer from hallucinations. They over-rely on partial cues and generate incorrect responses. Recently, methods like Visual Contrastive Decoding (VCD) and Instruction Contrasti…
HallucinationLanguage ModelingLanguage ModellingLarge Language Model+2Abstract Meaning Representation for Hospital Discharge Summarization
The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a l…
Abstract Meaning RepresentationHallucinationHKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs
Driven by the rapid progress in vision-language models (VLMs), the responsible behavior of large-scale multimodal models has become a prominent research area, particularly focusing on hallucination detection and factuali…
HallucinationKnowledge DistillationVL-GenRM: Enhancing Vision-Language Verification via Vision Experts and Iterative Training
Reinforcement Fine-Tuning (RFT) with verifiable rewards has advanced large language models but remains underexplored for Vision-Language (VL) models. The Vision-Language Reward Model (VL-RM) is key to aligning VL models …
HallucinationMultimodal ReasoningA Regret Perspective on Online Selective Generation
Large language generative models increasingly interact with humans, while their falsified responses raise concerns. To address this hallucination effect, selectively abstaining from answering, called selective generation…
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