Seeing Through the Fog: A Cost-Effectiveness Analysis of Hallucination Detection Systems
This paper presents a comparative analysis of hallucination detection systems for AI, focusing on automatic summarization and question answering tasks for Large Language Models (LLMs). We evaluate different hallucination detection systems using the diagnostic odds ratio (DOR) and cost-effectiveness metrics. Our results indicate that although advanced models can perform better they come at a much higher cost. We also demonstrate how an ideal hallucination detection system needs to maintain performance across different model sizes. Our findings highlight the importance of choosing a detection system aligned with specific application needs and resource constraints. Future research will explore hybrid systems and automated identification of underperforming components to enhance AI reliability and efficiency in detecting and mitigating hallucinations.
Code (0)
등록된 구현이 없습니다.
Tasks
DiagnosticHallucinationQuestion AnsweringSimilar Papers 제목 키워드 기반
ReLoop: "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 AnsweringSeeing Through the Chain: Mitigate Hallucination in Multimodal Reasoning Models via CoT Compression and Contrastive Preference Optimization
While multimodal reasoning models (MLRMs) have exhibited impressive capabilities, they remain prone to hallucinations, and effective solutions are still underexplored. In this paper, we experimentally analyze the halluci…
Multimodal ReasoningSeeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding
Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliability and practicality. Current approaches…
HallucinationObjectObject HallucinationSentence+2Seeing is Believing: Rich-Context Hallucination Detection for MLLMs via Backward Visual Grounding
Multimodal Large Language Models (MLLMs) have unlocked powerful cross-modal capabilities, but still significantly suffer from hallucinations. As such, accurate detection of hallucinations in MLLMs is imperative for ensur…
Visual GroundingMore Thinking, Less Seeing? Assessing Amplified Hallucination in Multimodal Reasoning Models
Test-time compute has empowered multimodal large language models to generate extended reasoning chains, yielding strong performance on tasks such as multimodal math reasoning. However, this improved reasoning ability oft…
DiagnosticHallucinationMathMultimodal Reasoning+1