VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
(2404.13874)Abstract
Large Vision-Language Models (LVLMs) suffer from hallucination issues, wherein the models generate plausible-sounding but factually incorrect outputs, undermining their reliability. A comprehensive quantitative evaluation is necessary to identify and understand the extent of hallucinations in these models. However, existing benchmarks are often limited in scope, focusing mainly on object hallucinations. Furthermore, current evaluation methods struggle to effectively address the subtle semantic distinctions between model outputs and reference data, as well as the balance between hallucination and informativeness. To address these issues, we introduce a multi-dimensional benchmark covering objects, attributes, and relations, with challenging images selected based on associative biases. Moreover, we propose an LLM-based two-stage evaluation framework that generalizes the popular CHAIR metric and incorporates both faithfulness and coverage into the evaluation. Experiments on 10 established LVLMs demonstrate that our evaluation metric is more comprehensive and better correlated with humans than existing work when evaluating on our challenging human annotated benchmark dataset. Our work also highlights the critical balance between faithfulness and coverage of model outputs, and encourages future works to address hallucinations in LVLMs while keeping their outputs informative.
Overview
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The paper introduces VALOR-Eval and VALOR-Bench, tools for evaluating hallucinations in Large Vision-Language Models (LVLMs), focusing on the accuracy and relevance of objects, attributes, and relationships described in images.
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VALOR-Eval uses a large language model in a two-stage process to improve hallucination detection in LVLMs, assessing both the faithfulness and coverage of the models' outputs.
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A comparative analysis with existing frameworks highlights the advantages of VALOR-Eval in providing a dynamic and scalable evaluation approach that surpasses the constraints of fixed vocabulary lists.
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The study underlines the importance of refining LVLMs for better accuracy and reliability, setting new standards in evaluation and opening pathways for future enhancements.
Holistic Evaluation of Large Vision-Language Models: Introducing VALOR-Eval and VALOR-Bench for Assessing Hallucination, Coverage, and Faithfulness
Introduction to the Paper's Contributions
The study presents a rigorous evaluation framework and benchmark, VALOR-Eval and VALOR-Bench, aimed at addressing the prevalent issue of hallucinations in Large Vision-Language Models (LVLMs). These hallucinations are misleading outputs where the model describes nonexistent objects or features within an image. The paper's contributions are multifaceted:
- VALOR-Bench: A new benchmark dataset comprised of human-annotated images. These images are carefully selected based on associative biases to challenge models on the accurate rendering of objects, attributes, and relationships.
- VALOR-Eval: An evaluation framework leverages a two-stage approach using a large language model to enhance the assessment of hallucinations in an open-vocabulary scenario, considering both the faithfulness and coverage of model outputs.
Key Findings from the Evaluation
The evaluation applied the VALOR-Eval framework across 10 LVLMs, revealing significant insights into the existing models' performance:
- The paper identifies a consistent issue across multiple models where there is a trade-off between precision and output scope. Some models showed high accuracy but limited coverage, suggesting a potential model bias towards being conservative in generating outputs to avoid errors.
- Despite advancements in model capabilities, the presence of hallucinations remains a critical issue. This problem underscores the need for more refined approaches in training and evaluating LVLMs.
Comparative Analysis with Existing Frameworks
The study provides a detailed analysis of previous hallucination evaluation methods, underscoring the limitations of current approaches that either focus narrowly on specific types of hallucinations or lack the integration of crucial metrics such as coverage. The new VALOR-Eval improves upon these by offering a comprehensive, nuanced, and scalable approach. This capability is attributed to its use of LLMs in identifying and matching hallucinated content more dynamically compared to fixed vocabulary lists used in conventional methods.
Implications and Future Directions
The implications of this research are profound for the development and refinement of LVLMs. The introduction of the VALOR-Bench dataset provides a robust tool for future studies, offering a platform to train and test models under challenging conditions designed to mimic real-world complexities.
Furthermore, the insights regarding the trade-offs between precision and coverage invite further exploration into model architectures and training processes that can balance these aspects more effectively. The field might also explore integrating these evaluation techniques directly into the training loop of LVLMs to directly address and mitigate hallucination during model development.
Concluding Thoughts
The VALOR-Eval framework and VALOR-Bench dataset set new standards for the evaluation of vision-language models, emphasizing the critical balance between hallucination control and output informativeness. This study not only advances our understanding of the limitations of current LVLMs but also charts a pathway for future enhancements in model accuracy and reliability. As LVLMs continue to permeate various technological and creative sectors, refining these models' ability to interpret and describe visual content accurately remains a paramount endeavor.
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