AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination
Evaluation
- URL: http://arxiv.org/abs/2311.07397v2
- Date: Fri, 23 Feb 2024 07:54:11 GMT
- Title: AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination
Evaluation
- Authors: Junyang Wang, Yuhang Wang, Guohai Xu, Jing Zhang, Yukai Gu, Haitao
Jia, Jiaqi Wang, Haiyang Xu, Ming Yan, Ji Zhang, Jitao Sang
- Abstract summary: Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations.
evaluating MLLMs' hallucinations is becoming increasingly important in model improvement and practical application deployment.
We propose an LLM-free multi-dimensional benchmark AMBER, which can be used to evaluate both generative task and discriminative task.
- Score: 58.19101663976327
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Despite making significant progress in multi-modal tasks, current Multi-modal
Large Language Models (MLLMs) encounter the significant challenge of
hallucinations, which may lead to harmful consequences. Therefore, evaluating
MLLMs' hallucinations is becoming increasingly important in model improvement
and practical application deployment. Previous works are limited in high
evaluation costs (e.g., relying on humans or advanced LLMs) and insufficient
evaluation dimensions (e.g., types of tasks and hallucinations). In this paper,
we propose an LLM-free multi-dimensional benchmark AMBER, which can be used to
evaluate both generative task and discriminative task including existence,
attribute and relation hallucination. Based on AMBER, we design a low-cost and
efficient evaluation pipeline. Additionally, we conduct a comprehensive
evaluation and detailed analysis of mainstream MLLMs including GPT-4V(ision),
and also give guideline suggestions for mitigating hallucinations. The data and
code of AMBER are available at https://github.com/junyangwang0410/AMBER.
Related papers
- Hallucination of Multimodal Large Language Models: A Survey [40.73148186369018]
multimodal large language models (MLLMs) have demonstrated significant advancements and remarkable abilities in multimodal tasks.
Despite these promising developments, MLLMs often generate outputs that are inconsistent with the visual content.
This survey aims to deepen the understanding of hallucinations in MLLMs and inspire further advancements in the field.
arXiv Detail & Related papers (2024-04-29T17:59:41Z) - Exploring and Evaluating Hallucinations in LLM-Powered Code Generation [14.438161741833687]
Large Language Models (LLMs) produce outputs that deviate from users' intent, exhibit internal inconsistencies, or misalign with factual knowledge.
Existing work mainly focuses on investing the hallucination in the domain of natural language generation.
We conduct a thematic analysis of the LLM-generated code to summarize and categorize the hallucinations present in it.
We propose HalluCode, a benchmark for evaluating the performance of code LLMs in recognizing hallucinations.
arXiv Detail & Related papers (2024-04-01T07:31:45Z) - HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination
Tendency of LLMs [0.0]
Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs)
This paper introduces an automated scalable framework that combines benchmarking LLMs' hallucination tendencies with efficient hallucination detection.
The framework is domain-agnostic, allowing the use of any language model for benchmark creation or evaluation in any domain.
arXiv Detail & Related papers (2024-02-25T22:23:37Z) - MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark [41.68821233828375]
This paper introduces a novel benchmark, termed MLLM-as-a-Judge, to assess the ability of MLLMs in assisting judges across diverse modalities.
Our study reveals that, while MLLMs demonstrate remarkable human-like discernment in Pair Comparison, there is a significant divergence from human preferences in Scoring Evaluation and Batch Ranking.
arXiv Detail & Related papers (2024-02-07T12:28:32Z) - 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 [44.401826163314716]
We propose a new evaluation paradigm for MLLMs using potent MLLM as the judge.
We benchmark 21 popular MLLMs in a pairwise-comparison fashion, showing diverse performance across models.
The validity of our benchmark manifests itself in reaching 88.02% agreement with human evaluation.
arXiv Detail & Related papers (2023-11-23T12:04:25Z) - Evaluation and Analysis of Hallucination in Large Vision-Language Models [49.19829480199372]
Large Vision-Language Models (LVLMs) have recently achieved remarkable success.
LVLMs are still plagued by the hallucination problem.
Hallucination refers to the information of LVLMs' responses that does not exist in the visual input.
arXiv Detail & Related papers (2023-08-29T08:51:24Z) - MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models [73.86954509967416]
Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks.
This paper presents the first comprehensive MLLM Evaluation benchmark MME.
It measures both perception and cognition abilities on a total of 14 subtasks.
arXiv Detail & Related papers (2023-06-23T09:22:36Z) - LVLM-eHub: A Comprehensive Evaluation Benchmark for Large
Vision-Language Models [55.304181390027274]
This paper presents a comprehensive evaluation of publicly available large multimodal models by building a LVLM evaluation Hub (LVLM-eHub)
Our LVLM-eHub consists of $8$ representative LVLMs such as InstructBLIP and MiniGPT-4, which are thoroughly evaluated by a quantitative capability evaluation and an online arena platform.
The study reveals several innovative findings. First, instruction-tuned LVLM with massive in-domain data such as InstructBLIP heavily overfits many existing tasks, generalizing poorly in the open-world scenario.
arXiv Detail & Related papers (2023-06-15T16:39:24Z) - Evaluating Object Hallucination in Large Vision-Language Models [122.40337582958453]
This work presents the first systematic study on object hallucination of large vision-language models (LVLMs)
We find that LVLMs tend to generate objects that are inconsistent with the target images in the descriptions.
We propose a polling-based query method called POPE to evaluate the object hallucination.
arXiv Detail & Related papers (2023-05-17T16:34:01Z)
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.