RETLLM: Training and Data-Free MLLMs for Multimodal Information Retrieval

• URL: http://arxiv.org/abs/2602.22278v1
• Date: Wed, 25 Feb 2026 10:31:32 GMT
• Title: RETLLM: Training and Data-Free MLLMs for Multimodal Information Retrieval
• Authors: Dawei Su, Dongsheng Wang,
• Abstract summary: Multimodal information retrieval (MMIR) has gained attention for its flexibility in handling text, images, or mixed queries and candidates.<n>Recent breakthroughs in multimodal large language models (MLLMs) boost MMIR performance by incorporating MLLM knowledge under the contrastive finetuning framework.<n>We introduce a novel framework, RetLLM, designed to query MLLMs for MMIR in a training- and data-free manner.
• Score: 2.2125276321198677
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal information retrieval (MMIR) has gained attention for its flexibility in handling text, images, or mixed queries and candidates. Recent breakthroughs in multimodal large language models (MLLMs) boost MMIR performance by incorporating MLLM knowledge under the contrastive finetuning framework. However, they suffer from pre-training inconsistency and require large datasets. In this work, we introduce a novel framework, RetLLM, designed to query MLLMs for MMIR in a training- and data-free manner. Specifically, we formulate MMIR as a similarity score generation task and prompt MLLMs to directly predict retrieval scores in a coarse-then-fine pipeline. At the coarse stage, a top-k filtering strategy builds a small yet high-quality candidate pool for each query, enabling MLLMs to focus on semantically relevant candidates. Subsequently, the retrieval score is predicted by feeding both the query and candidate into MLLMs at the fine stage. Importantly, we propose a visual enhancement module during reasoning to help MLLMs re-pick forgotten visuals, improving retrieval. Extensive experiments on MMIR benchmarks show that RetLLM outperforms fine-tuned models. Ablation studies further verify each component. Our work demonstrates that MLLMs can achieve strong MMIR performance without any training, highlighting their inherent multimodal reasoning ability in a simple, scalable framework. We release our code at: https://github.com/alivecat05/RETLLM

Related papers

• FewMMBench: A Benchmark for Multimodal Few-Shot Learning [17.747746608503114]
  FewMMBench is a comprehensive benchmark designed to evaluate multimodal large language models (MLLMs) under few-shot conditions.<n>We evaluate 26 open-weight MLLMs from six model families across zero-shot, few-shot, and CoT-augmented few-shot settings.<n>Our findings reveal that instruction-tuned models exhibit strong zero-shot performance but benefit minimally, or even regress, with additional demonstrations or CoT reasoning.
  arXiv  Detail & Related papers  (2026-02-25T12:30:18Z)
• NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints [100.02131897927484]
  This paper focuses on the native training of Multimodal Large Language Models (MLLMs) in an end-to-end manner.<n>We propose a native MLLM called NaViL, combined with a simple and cost-effective recipe.<n> Experimental results on 14 multimodal benchmarks confirm the competitive performance of NaViL against existing MLLMs.
  arXiv  Detail & Related papers  (2025-10-09T17:59:37Z)
• Improve MLLM Benchmark Efficiency through Interview [41.51050382338298]
  Full-coverage Q&A testing on large-scale data is resource-intensive and time-consuming.<n>We propose the MLLM Interview strategy, which aims to quickly obtain MLLM performance metrics by quizzing fewer question.
  arXiv  Detail & Related papers  (2025-06-01T07:51:15Z)
• Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers [74.17516978246152]
  Large language models (LLMs) have been widely integrated into information retrieval to advance traditional techniques.<n>We propose EXSEARCH, an agentic search framework, where the LLM learns to retrieve useful information as the reasoning unfolds.<n>Experiments on four knowledge-intensive benchmarks show that EXSEARCH substantially outperforms baselines.
  arXiv  Detail & Related papers  (2025-05-26T15:27:55Z)
• MM-Embed: Universal Multimodal Retrieval with Multimodal LLMs [78.5013630951288]
  This paper introduces techniques for advancing information retrieval with multimodal large language models (MLLMs)<n>We first study fine-tuning an MLLM as a bi-encoder retriever on 10 datasets with 16 retrieval tasks.<n>Our model, MM-Embed, achieves state-of-the-art performance on the multimodal retrieval benchmark M-BEIR.
  arXiv  Detail & Related papers  (2024-11-04T20:06:34Z)
• MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents [28.419007116364668]
  MLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data.<n>Current retrieval methods primarily focus on surface-level similarities of textual or visual cues in trajectories, neglecting their effectiveness for the specific task at hand.<n>We propose a novel method, MLLM As ReTriever (MART), which enhances the performance of embodied agents by utilizing interaction data.
  arXiv  Detail & Related papers  (2024-10-04T14:10:39Z)
• The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective [53.48484062444108]
  We find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs; on the other hand, MLLMs can facilitate the development of data. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective.
  arXiv  Detail & Related papers  (2024-07-11T15:08:11Z)
• InfMLLM: A Unified Framework for Visual-Language Tasks [44.29407348046122]
  multimodal large language models (MLLMs) have attracted growing interest. This work delves into enabling LLMs to tackle more vision-language-related tasks. InfMLLM achieves either state-of-the-art (SOTA) performance or performance comparable to recent MLLMs.
  arXiv  Detail & Related papers  (2023-11-12T09:58:16Z)
• MLLM-DataEngine: An Iterative Refinement Approach for MLLM [62.30753425449056]
  We propose a novel closed-loop system that bridges data generation, model training, and evaluation. Within each loop, the MLLM-DataEngine first analyze the weakness of the model based on the evaluation results. For targeting, we propose an Adaptive Bad-case Sampling module, which adjusts the ratio of different types of data. For quality, we resort to GPT-4 to generate high-quality data with each given data type.
  arXiv  Detail & Related papers  (2023-08-25T01:41:04Z)
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models [111.51612340032052]
  Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks.<n>This paper presents the first comprehensive MLLM Evaluation benchmark MME.<n>It measures both perception and cognition abilities on a total of 14 subtasks.
  arXiv  Detail & Related papers  (2023-06-23T09:22:36Z)

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.