VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation (2412.10151v1)

Abstract: We propose the VLR-Bench, a visual question answering (VQA) benchmark for evaluating vision LLMs (VLMs) based on retrieval augmented generation (RAG). Unlike existing evaluation datasets for external knowledge-based VQA, the proposed VLR-Bench includes five input passages. This allows testing of the ability to determine which passage is useful for answering a given query, a capability lacking in previous research. In this context, we constructed a dataset of 32,000 automatically generated instruction-following examples, which we denote as VLR-IF. This dataset is specifically designed to enhance the RAG capabilities of VLMs by enabling them to learn how to generate appropriate answers based on input passages. We evaluated the validity of the proposed benchmark and training data and verified its performance using the state-of-the-art Llama3-based VLM, the Llava-Llama-3 model. The proposed VLR-Bench and VLR-IF datasets are publicly available online.

• The paper introduces VLR-Bench for assessing vision-language models through retrieval-augmented generation using multilingual datasets.
• It employs 300 dataset partitions and 32,000 VLR-IF examples to challenge models in identifying key 'gold passages' among distractors.
• Benchmark results underscore the importance of retrieval mechanisms in boosting model performance across different languages and architectures.

Overview of "VLR-Bench: Multilingual Benchmark Dataset for Vision-Language Retrieval Augmented Generation"

The paper introduces VLR-Bench, a novel multilingual benchmark designed to evaluate the Retrieval-Augmented Generation (RAG) capabilities of Vision-LLMs (VLMs). This benchmark addresses a critical gap in existing evaluation methods by incorporating multifaceted input passages, enabling more precise assessments of VLMs' ability to utilize external knowledge effectively. Notably, the reliance on retrieval mechanisms is paramount, since directly generating answers from images alone often lacks the required completeness and accuracy.

Benchmark Structure and Dataset Composition

VLR-Bench includes a collection of 300 datasets partitioned into general knowledge and culturally specific information from English, Korean, and Chinese discourse. Each dataset presents an image, an associated query, and five passages, of which only two contain directly relevant information—termed as "gold passages." This setup challenges VLMs to discern usable knowledge from distractors, which is critical in practical AI applications.

To complement VLR-Bench, the authors also developed VLR-IF (VLR Instruction Following), a dataset comprising 32,000 examples aimed at improving VLMs' capacity for retrieval-augmented answer generation. The data generation process involves a rigorous manual review and annotation process to ensure high relevancy and diversity.

Evaluation and Findings

The authors evaluated VLR-Bench using several state-of-the-art models, including Llama3-based and GPT-4o models. The results underline the importance of external knowledge retrieval, as indicated by the significant performance declines observed when input passages were excluded. Moreover, the incorporation of the VLR-IF training data resulted in substantial performance enhancements, showcasing its efficacy in elevating models’ ability to leverage external knowledge effectively.

The paper also revealed notable disparities in performances across models and languages, emphasizing the complexities inherent in multilingual VLM benchmarks. Interestingly, the qualitative evaluation using GPT-4o corroborated the quantitative findings, affirming the benchmark's robustness and the potential for future extensions to encompass more diverse datasets.

Implications and Future Directions

The implications of this research are multifaceted. Practically, the VLR-Bench and VLR-IF datasets aid in assessing and improving VLMs' proficiency in utilizing external knowledge, which is crucial for developing more refined AI systems capable of handling open-domain queries. Theoretically, the benchmark sheds light on challenges like the retrieval of relevant passages amid distractors, which could inform the design of future VLM architectures and retrieval strategies.

The authors acknowledge certain limitations, such as the current lack of image search capabilities within the dataset and potential difficulties in expanding the multilingual aspect cost-effectively. Addressing these limitations could open new avenues for exploration, potentially involving the integration of more advanced retrieval algorithms or extending the benchmarks to a broader spectrum of languages and cultural contexts.

In conclusion, the VLR-Bench is a significant contribution to the domain of vision-language integration, offering both an evaluative mechanism and insights into the intricate interaction between visual inputs and retrieval-augmented generation in AI models. The research paves the way for future enhancements and expansions that will further refine the capabilities of multilingual VLMs in real-world settings.