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arxiv: 2307.16125 · v2 · submitted 2023-07-30 · 💻 cs.CL · cs.CV

Recognition: 2 theorem links

· Lean Theorem

SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Bohao Li , Rui Wang , Guangzhi Wang , Yuying Ge , Yixiao Ge , Ying Shan
Authors on Pith no claims yet

Pith reviewed 2026-05-12 16:55 UTC · model grok-4.3

classification 💻 cs.CL cs.CV
keywords multimodal LLMsgenerative comprehensionbenchmarkimage understandingvideo understandingmultiple choice evaluationspatial and temporal reasoning
0
0 comments X

The pith

SEED-Bench supplies 19K human-verified multiple-choice questions to measure multimodal LLMs on image and video comprehension across 12 dimensions.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to create an objective way to test the generative comprehension skills of multimodal large language models. It builds a benchmark six times larger than prior ones, with questions that cover both static images and dynamic videos. A pipeline combines automatic generation with human checks to produce reliable multiple-choice items whose correct answers come directly from annotations. This setup allows models to be scored without needing extra human or model judges at evaluation time. Testing eighteen existing models then shows where current systems fall short in spatial and temporal understanding.

Core claim

SEED-Bench consists of 19K multiple choice questions with accurate human annotations, which spans 12 evaluation dimensions including the comprehension of both the image and video modality, enabling an objective and efficient assessment of model performance without human or GPT intervention during evaluation.

What carries the argument

The pipeline that generates multiple-choice questions targeting specific dimensions through automatic filtering followed by manual verification.

If this is right

  • Evaluating 18 models across all 12 dimensions reveals concrete limitations in current MLLMs for both spatial and temporal understanding.
  • The benchmark supports consistent leaderboard tracking that lets the community compare progress without repeated human judgment.
  • Insights from the results can directly motivate targeted improvements in models that handle image and video modalities together.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Widespread use of this benchmark could make cross-model comparisons more reliable by fixing the question set and scoring method.
  • The scale and verification process may encourage development of models that maintain performance when questions shift from multiple choice to free-form generation.
  • Extending similar pipelines to new modalities could help identify whether comprehension gaps are modality-specific or general.

Load-bearing premise

The questions produced by automatic generation plus manual verification actually test genuine generative comprehension instead of artifacts from the creation process.

What would settle it

An experiment showing that models scoring highest on SEED-Bench still fail to produce accurate open-ended descriptions or answers on the same image and video content.

read the original abstract

Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment of generative models, by introducing a benchmark named SEED-Bench. SEED-Bench consists of 19K multiple choice questions with accurate human annotations (x 6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality. We develop an advanced pipeline for generating multiple-choice questions that target specific evaluation dimensions, integrating both automatic filtering and manual verification processes. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 18 models across all 12 dimensions, covering both the spatial and temporal understanding. By revealing the limitations of existing MLLMs through evaluation results, we aim for SEED-Bench to provide insights for motivating future research. We will launch and consistently maintain a leaderboard to provide a platform for the community to assess and investigate model capability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces SEED-Bench, a benchmark of 19K multiple-choice questions with human annotations for evaluating generative comprehension in Multimodal LLMs (MLLMs). It spans 12 dimensions covering spatial and temporal understanding of both image and video modalities, constructed via an automatic question-generation pipeline with filtering and manual verification. The authors evaluate 18 existing MLLMs on the benchmark, reveal their limitations, and announce a public leaderboard.

Significance. If validated to require genuine multimodal input, SEED-Bench would be a meaningful contribution due to its scale (six times larger than prior benchmarks) and broad coverage of 12 dimensions. A well-controlled benchmark of this size could standardize evaluation of MLLM comprehension and guide improvements in visual-language integration.

major comments (3)
  1. [Section 3] Benchmark construction (Section 3): The pipeline description provides no quantitative evidence that questions cannot be solved from question text and options alone (e.g., no text-only baseline accuracy reported, no ablation removing images/videos). This directly undermines the central claim that performance measures multimodal comprehension rather than language priors.
  2. [Section 3.2] Annotation process (Section 3.2): No inter-annotator agreement statistics or details on how the 12 evaluation dimensions were selected and operationalized are reported, weakening confidence that the 19K questions reliably target the intended spatial/temporal capabilities.
  3. [Section 4] Evaluation results (Section 4): The reported model scores lack analysis of whether errors correlate with visual content (e.g., via attention maps or controlled perturbations); without this, it is unclear whether the benchmark isolates the claimed generative comprehension limitations.
minor comments (2)
  1. [Abstract] The abstract and introduction repeat the 'x6 larger' claim without citing the exact sizes of the compared benchmarks.
  2. [Figure 1] Figure 1 caption could more explicitly label the 12 dimensions and their image/video split for quick reference.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which help strengthen the manuscript. We address each major comment point by point below, agreeing where revisions are warranted and providing clarifications where the existing work already supports our claims. We will update the paper accordingly in the revised version.

read point-by-point responses

  1. Referee: [Section 3] Benchmark construction (Section 3): The pipeline description provides no quantitative evidence that questions cannot be solved from question text and options alone (e.g., no text-only baseline accuracy reported, no ablation removing images/videos). This directly undermines the central claim that performance measures multimodal comprehension rather than language priors.

    Authors: We agree that explicit quantitative validation is important to confirm the benchmark requires multimodal input. Although the questions are generated from visual content with human-annotated ground truth and filtered to target specific visual dimensions, we did not report a text-only baseline in the original submission. In the revised manuscript, we will add evaluations of multiple models on the text-only version of SEED-Bench, demonstrating substantially lower accuracy without images or videos. This will directly support that the benchmark measures generative multimodal comprehension rather than language priors alone. revision: yes

  2. Referee: [Section 3.2] Annotation process (Section 3.2): No inter-annotator agreement statistics or details on how the 12 evaluation dimensions were selected and operationalized are reported, weakening confidence that the 19K questions reliably target the intended spatial/temporal capabilities.

    Authors: We acknowledge the value of reporting inter-annotator agreement to increase confidence in the annotations. We will add these statistics (e.g., agreement rates across the manual verification step) to the revised Section 3.2. The 12 dimensions were selected to comprehensively cover spatial and temporal understanding for both images and videos, drawing from established categories in visual reasoning and video comprehension literature. We will expand the description of how each dimension is operationalized through targeted question templates and examples in the updated manuscript. revision: yes

  3. Referee: [Section 4] Evaluation results (Section 4): The reported model scores lack analysis of whether errors correlate with visual content (e.g., via attention maps or controlled perturbations); without this, it is unclear whether the benchmark isolates the claimed generative comprehension limitations.

    Authors: This is a fair point for deeper validation of error sources. The current results already show systematic weaknesses across models on specific dimensions (e.g., temporal reasoning), which we attribute to multimodal integration challenges based on the question design. However, attention map analysis or systematic perturbations would require additional experiments not included in this benchmark-focused work. In the revision, we will incorporate a qualitative error analysis with example cases linking failures to visual elements, along with a discussion of how such analyses could be pursued in future work. revision: partial

Circularity Check

0 steps flagged

No circularity: benchmark construction is descriptive and externally verifiable

full rationale

The paper introduces SEED-Bench via an explicit pipeline of automatic question generation, filtering, and human annotation/verification to produce 19K MCQs across 12 dimensions. No equations, fitted parameters, predictions, or derivations are claimed. The central claim (that the resulting questions enable objective evaluation of MLLM comprehension) rests on the described human-verified ground truth rather than reducing to self-definition or self-citation. Evaluation of 18 external models occurs after benchmark creation, providing an independent test. This matches the default expectation of a self-contained benchmark paper with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical benchmark paper with no mathematical derivations, fitted parameters, or new postulated entities; it relies on standard machine-learning evaluation practices and human annotation.

pith-pipeline@v0.9.0 · 5536 in / 1044 out tokens · 29072 ms · 2026-05-12T16:55:12.675278+00:00 · methodology

discussion (0)

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