FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks

• URL: http://arxiv.org/abs/2410.01089v1
• Date: Tue, 1 Oct 2024 21:38:15 GMT
• Title: FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks
• Authors: Peiran Wu, Che Liu, Canyu Chen, Jun Li, Cosmin I. Bercea, Rossella Arcucci,
• Abstract summary: We propose FMBench, the first benchmark designed to evaluate the fairness of MLLMs performance across diverse demographic attributes. We thoroughly evaluate the performance and fairness of eight state-of-the-art open-source MLLMs, including both general and medical. All data and code will be released upon acceptance.
• Score: 11.094602017349928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advancements in Multimodal Large Language Models (MLLMs) have significantly improved medical task performance, such as Visual Question Answering (VQA) and Report Generation (RG). However, the fairness of these models across diverse demographic groups remains underexplored, despite its importance in healthcare. This oversight is partly due to the lack of demographic diversity in existing medical multimodal datasets, which complicates the evaluation of fairness. In response, we propose FMBench, the first benchmark designed to evaluate the fairness of MLLMs performance across diverse demographic attributes. FMBench has the following key features: 1: It includes four demographic attributes: race, ethnicity, language, and gender, across two tasks, VQA and RG, under zero-shot settings. 2: Our VQA task is free-form, enhancing real-world applicability and mitigating the biases associated with predefined choices. 3: We utilize both lexical metrics and LLM-based metrics, aligned with clinical evaluations, to assess models not only for linguistic accuracy but also from a clinical perspective. Furthermore, we introduce a new metric, Fairness-Aware Performance (FAP), to evaluate how fairly MLLMs perform across various demographic attributes. We thoroughly evaluate the performance and fairness of eight state-of-the-art open-source MLLMs, including both general and medical MLLMs, ranging from 7B to 26B parameters on the proposed benchmark. We aim for FMBench to assist the research community in refining model evaluation and driving future advancements in the field. All data and code will be released upon acceptance.

Related papers

• MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [71.36392373876505]
  We introduce MMIE, a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs) MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts. It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies.
  arXiv  Detail & Related papers  (2024-10-14T04:15:00Z)
• VHELM: A Holistic Evaluation of Vision Language Models [75.88987277686914]
  We present the Holistic Evaluation of Vision Language Models (VHELM) VHELM aggregates various datasets to cover one or more of the 9 aspects: visual perception, knowledge, reasoning, bias, fairness, multilinguality, robustness, toxicity, and safety. Our framework is designed to be lightweight and automatic so that evaluation runs are cheap and fast.
  arXiv  Detail & Related papers  (2024-10-09T17:46:34Z)
• FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models [37.803490266325]
  We introduce FairMedFM, a fairness benchmark for foundation models (FMs) research in medical imaging. FairMedFM integrates with 17 popular medical imaging datasets, encompassing different modalities, dimensionalities, and sensitive attributes. It explores 20 widely used FMs, with various usages such as zero-shot learning, linear probing, parameter-efficient fine-tuning, and prompting in various downstream tasks -- classification and segmentation.
  arXiv  Detail & Related papers  (2024-07-01T05:47:58Z)
• DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain [8.246368441549967]
  We present the first-ever publicly available French biomedical language understanding benchmark called DrBenchmark. It encompasses 20 diversified tasks, including named-entity recognition, part-of-speech tagging, question-answering, semantic textual similarity, and classification. We evaluate 8 state-of-the-art pre-trained masked language models (MLMs) on general and biomedical-specific data, as well as English specifics to assess their cross-lingual capabilities.
  arXiv  Detail & Related papers  (2024-02-20T23:54:02Z)
• MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Models [1.3535643703577176]
  MultiMedEval is an open-source toolkit for fair and reproducible evaluation of large, medical vision-language models (VLM) It comprehensively assesses the models' performance on a broad array of six multi-modal tasks, conducted over 23 datasets, and spanning over 11 medical domains. We open-source a Python toolkit with a simple interface and setup process, enabling the evaluation of any VLM in just a few lines of code.
  arXiv  Detail & Related papers  (2024-02-14T15:49:08Z)
• PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging [8.043625583479598]
  Multimodal large language models (MLLMs) represent an evolutionary expansion in the capabilities of traditional large language models. Recent works investigate the adaptation of MLLMs as a universal solution to address medical multi-modal problems as a generative task. We propose a parameter efficient framework for fine-tuning MLLMs, specifically validated on medical visual question answering (Med-VQA) and medical report generation (MRG) tasks.
  arXiv  Detail & Related papers  (2024-01-05T13:22:12Z)
• SEED-Bench-2: Benchmarking Multimodal Large Language Models [67.28089415198338]
  Multimodal large language models (MLLMs) have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs. SEED-Bench-2 comprises 24K multiple-choice questions with accurate human annotations, which spans 27 dimensions. We evaluate the performance of 23 prominent open-source MLLMs and summarize valuable observations.
  arXiv  Detail & Related papers  (2023-11-28T05:53:55Z)
• MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria [49.500322937449326]
  Multimodal large language models (MLLMs) have broadened the scope of AI applications. Existing automatic evaluation methodologies for MLLMs are mainly limited in evaluating queries without considering user experiences. We propose a new evaluation paradigm for MLLMs, which is evaluating MLLMs with per-sample criteria using potent MLLM as the judge.
  arXiv  Detail & Related papers  (2023-11-23T12:04:25Z)
• ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models [49.48109472893714]
  Multimodal Large Language Models (MLLMs) have shown impressive abilities in interacting with visual content with myriad potential downstream tasks. We present the first Comprehensive Evaluation Framework (ChEF) that can holistically profile each MLLM and fairly compare different MLLMs. We will publicly release all the detailed implementations for further analysis, as well as an easy-to-use modular toolkit for the integration of new recipes and models.
  arXiv  Detail & Related papers  (2023-11-05T16:01:40Z)
• MMBench: Is Your Multi-modal Model an All-around Player? [114.45702807380415]
  We propose MMBench, a benchmark for assessing the multi-modal capabilities of vision-language models. MMBench is meticulously curated with well-designed quality control schemes. MMBench incorporates multiple-choice questions in both English and Chinese versions.
  arXiv  Detail & Related papers  (2023-07-12T16:23:09Z)
• Large Language Models Encode Clinical Knowledge [21.630872464930587]
  Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning.
  arXiv  Detail & Related papers  (2022-12-26T14:28:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.