Challenge: Existing work focuses on generating citations for text-only content . experimental results reveal MLLMs struggle to ground outputs reliably when handling multimodal input .
Approach: They propose a benchmark to assess the ability of MLLMs to generate text with citations in multimodal contexts.
Outcome: The proposed benchmark assesses the ability of MLLMs to generate text with citations in multimodal contexts.

Similar Papers

MIBench: Evaluating Multimodal Large Language Models over Multiple Images (2024.emnlp-main)

Copied to clipboard

Challenge: Existing benchmarks and MLLMs focus on single-image input scenarios, leaving performance of ML models when handling multiple images underexplored.
Approach: They propose a benchmark to evaluate fine-grained abilities of multimodal large language models in multi-image scenarios.
Outcome: The proposed benchmark categorizes the multi-image abilities into three scenarios: MII, MKS and MIC.
MLLM-Bench: Evaluating Multimodal LLMs with Per-sample Criteria (2025.naacl-long)

Copied to clipboard

Challenge: Existing evaluation methodologies for multimodal large language models are limited in evaluating objective queries without considering real-world user experiences.
Approach: They propose to evaluate multimodal large language models with per-sample criteria using potent MLLM as the judge.
Outcome: The proposed evaluation paradigm shows that it can be used to evaluate multimodal large language models with per-sample criteria.
WebUIBench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in WebUI-to-Code (2025.findings-acl)

Copied to clipboard

Challenge: Existing benchmarks for large language models focus on webpage generation outcomes.
Approach: They propose a multi-view evaluation framework to evaluate MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code.
Outcome: The proposed framework evaluates MLLMs in four key areas: WebUI Perception, HTML Programming, WebUI-HTML Understanding, and WebUI to code.
GODBench: A Benchmark for Multimodal Large Language Models in Video Comment Art (2025.acl-long)

Copied to clipboard

Challenge: Existing benchmarks for video comment art are constrained by their limited modalities and insufficient categories, hindering creativity in video-based comment art creation.
Approach: They propose a benchmark that integrates video and text modalities to evaluate MLLMs’ abilities to compose video Comment art.
Outcome: The proposed framework integrates video and text modalities to evaluate MLLMs’ abilities to compose video comment art.
MANBench: Is Your Multimodal Model Smarter than Human? (2025.findings-acl)

Copied to clipboard

Challenge: Multimodal Large Language Models (MLLMs) have been gaining popularity in multimodal tasks . a bilingual benchmark is available for MLLM users to evaluate their multimodal capabilities .
Approach: They propose a bilingual multimodal ability norms benchmark that measures multimodality across nine tasks.
Outcome: The proposed benchmark compared human performance against state-of-the-art MLLMs.
REFINESUMM: Self-Refining MLLM for Generating a Multimodal Summarization Dataset (2024.acl-long)

Copied to clipboard

Challenge: generating accurate and faithful multimodal summaries is challenging due to lack of appropriate multimodal datasets . large language models excel at synthesizing key information from diverse sources, but lack of adequate multimodal data sets for fine-tuning .
Approach: They propose a dataset specifically designed for image-text multimodal summarization . they generate summaries from Wikipedia sections and corresponding images and evaluate them .
Outcome: The proposed dataset improves summary quality by training a critic model on human annotations and using its predictions to remove low-quality summaries.
Multimodal Inconsistency Reasoning (MMIR): A New Benchmark for Multimodal Reasoning Models (2025.findings-acl)

Copied to clipboard

Challenge: Existing Multimodal Large Language Models (MLLMs) are predominantly trained on consistent visual-textual inputs, leaving open the question of whether they can handle semantic mismatches in layout-rich content.
Approach: They propose to use multimodal inconsistency reasoning to assess MLLMs' ability to reason about semantic mismatches in webpages, presentation slides, and posters.
Outcome: The proposed model outperforms open-source models in detecting inconsistencies in webpages, presentation slides, and posters while remaining vulnerable to inconsistent errors.
MCS-Bench: A Comprehensive Benchmark for Evaluating Multimodal Large Language Models in Chinese Classical Studies (2025.acl-long)

Copied to clipboard

Challenge: Multimodal Large Language Models (MLLMs) have advanced visual and language understanding, but their potential in Chinese Classical Studies (CCS) remains underexplored due to the lack of specialized benchmarks.
Approach: They propose to develop a multimodal benchmark specifically designed for Chinese Classical Studies across multiple subdomains to bridge this gap.
Outcome: The proposed benchmark spans seven core subdomains with a total of 45 meticulously designed tasks.
Thesis Proposal: Multimodal Benchmark for Music Understanding in Large Language Models (2026.eacl-srw)

Copied to clipboard

Challenge: Existing music-focused benchmarks are fragmented, largely single-modality, Western-centric . existing methods for evaluating MLLMs are lacking reproducibility and reliability .
Approach: They propose to develop a musically multimodal benchmark that will integrate music into the benchmark.
Outcome: The proposed benchmark will integrate culturally diverse musical material beyond the dominant Western canon.
MM-SOC: Benchmarking Multimodal Large Language Models in Social Media Platforms (2024.findings-acl)

Copied to clipboard

Challenge: Social media platforms are hubs for multimodal information exchange, encompassing text, images, and videos, making it challenging for machines to comprehend the information or emotions associated with interactions in online spaces.
Approach: They propose a benchmark to evaluate MLLMs' understanding of multimodal social media content and a large-scale YouTube tagging dataset to evaluate their performance.
Outcome: The proposed model performs better in a zero-shot setting, suggesting potential improvements.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations