# Multi-LLM Collaborative Caption Generation in Scientific Documents

arXiv: https://arxiv.org/abs/2501.02552 · HTML: https://mlsanity.com/paper/2501.02552/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 21 of 21)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://mlsanity.com/paper/2408.01800/) (2024)
- [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [FigureQA: An Annotated Figure Dataset for Visual Reasoning](https://mlsanity.com/paper/1710.07300/) (2017)
- [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://mlsanity.com/paper/2310.05736/) (2023)
- [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://mlsanity.com/paper/2210.07197/) (2022)
- [SciCap: Generating Captions for Scientific Figures](https://mlsanity.com/paper/2110.11624/) (2021)
- [Getting MoRE out of Mixture of Language Model Reasoning Experts](https://mlsanity.com/paper/2305.14628/) (2023)
- [CLAIR: Evaluating Image Captions with Large Language Models](https://mlsanity.com/paper/2310.12971/) (2023)
- [LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extraction](https://mlsanity.com/paper/2403.00863/) (2024)
- [SciCap+: A Knowledge Augmented Dataset to Study the Challenges of Scientific Figure Captioning](https://mlsanity.com/paper/2306.03491/) (2023)
- [GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions](https://mlsanity.com/paper/2310.15405/) (2023)
- [Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization](https://mlsanity.com/paper/2302.12324/) (2023)
- [SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings](https://mlsanity.com/paper/2403.17784/) (2024)
- [The Solution for the ICCV 2023 1st Scientific Figure Captioning Challenge](https://mlsanity.com/paper/2403.17342/) (2024)
- [Proposal Report for the 2nd SciCAP Competition 2024](https://mlsanity.com/paper/2407.01897/) (2024)

## Built on by (most cited first, 6 of 6)

- [VER-Bench: Evaluating MLLMs on Reasoning with Fine-Grained Visual Evidence](https://mlsanity.com/paper/2508.04852/) (2025)
- [Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023](https://mlsanity.com/paper/2501.19353/) (2025)
- [LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles](https://mlsanity.com/paper/2506.06561/) (2025)
- [Taxonomic Reasoning for Rare Arthropods: Combining Dense Image Captioning and RAG for Interpretable Classification](https://mlsanity.com/paper/2503.10886/) (2025)
- [Accepted with Minor Revisions: Value of AI-Assisted Scientific Writing](https://mlsanity.com/paper/2511.12529/) (2025)
- [Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation](https://mlsanity.com/paper/2511.13476/) (2025)

## Abstract (arXiv)

Scientific figure captioning is a complex task that requires generating contextually appropriate descriptions of visual content. However, existing methods often fall short by utilizing incomplete information, treating the task solely as either an image-to-text or text summarization problem. This limitation hinders the generation of high-quality captions that fully capture the necessary details. Moreover, existing data sourced from arXiv papers contain low-quality captions, posing significant challenges for training large language models (LLMs). In this paper, we introduce a framework called Multi-LLM Collaborative Figure Caption Generation (MLBCAP) to address these challenges by leveraging specialized LLMs for distinct sub-tasks. Our approach unfolds in three key modules: (Quality Assessment) We utilize multimodal LLMs to assess the quality of training data, enabling the filtration of low-quality captions. (Diverse Caption Generation) We then employ a strategy of fine-tuning/prompting multiple LLMs on the captioning task to generate candidate captions. (Judgment) Lastly, we prompt a prominent LLM to select the highest quality caption from the candidates, followed by refining any remaining inaccuracies. Human evaluations demonstrate that informative captions produced by our approach rank better than human-written captions, highlighting its effectiveness. Our code is available at https://github.com/teamreboott/MLBCAP