# VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization

arXiv: https://arxiv.org/abs/2402.07398 · HTML: https://mlsanity.com/paper/2402.07398/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 12 Feb 2024.

## Placed in

- Multimodal LLMs / vision-language models / [MLLM training and visual instruction tuning](https://mlsanity.com/mllm/mllm-training-and-visual-instruction-tuning/)

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [VizWiz Grand Challenge: Answering Visual Questions from Blind People](https://mlsanity.com/paper/1802.08218/) (2018)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://mlsanity.com/paper/2209.06794/) (2022)
- [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022)
- [An Empirical Study of Example Forgetting during Deep Neural Network Learning](https://mlsanity.com/paper/1812.05159/) (2018)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [nocaps: novel object captioning at scale](https://mlsanity.com/paper/1812.08658/) (2018)
- [Visual Dialog](https://mlsanity.com/paper/1611.08669/) (2016)
- [IconQA: A New Benchmark for Abstract Diagram Understanding and Visual Language Reasoning](https://mlsanity.com/paper/2110.13214/) (2021)
- [BLIVA: A Simple Multimodal LLM for Better Handling of Text-Rich Visual Questions](https://mlsanity.com/paper/2308.09936/) (2023)
- [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://mlsanity.com/paper/2303.08518/) (2023)
- [LAVIS: A Library for Language-Vision Intelligence](https://mlsanity.com/paper/2209.09019/) (2022)
- [What Makes Pre-trained Language Models Better Zero-shot Learners?](https://mlsanity.com/paper/2209.15206/) (2022)
- [Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens](https://mlsanity.com/paper/2310.13220/) (2023)

## Built on by (most cited first, 16 of 16)

- [I2EBench: A Comprehensive Benchmark for Instruction-based Image Editing](https://mlsanity.com/paper/2408.14180/) (2024)
- [A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding](https://mlsanity.com/paper/2407.01976/) (2024)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [The Evolution of Multimodal Model Architectures](https://mlsanity.com/paper/2405.17927/) (2024)
- [Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection](https://mlsanity.com/paper/2411.07446/) (2024)
- [Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation](https://mlsanity.com/paper/2501.12432/) (2025)
- [CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation](https://mlsanity.com/paper/2411.04679/) (2024)
- [What Makes a Good Natural Language Prompt?](https://mlsanity.com/paper/2506.06950/) (2025)
- [Smart Vision-Language Reasoners](https://mlsanity.com/paper/2407.04212/) (2024)
- [Large Language Models for Zero-Shot Multicultural Name Recognition](https://mlsanity.com/paper/2507.04149/) (2025)
- [LumiGen: An LVLM-Enhanced Iterative Framework for Fine-Grained Text-to-Image Generation](https://mlsanity.com/paper/2508.04732/) (2025)
- [LVLM-Composer's Explicit Planning for Image Generation](https://mlsanity.com/paper/2507.04152/) (2025)
- [VisuCraft: Enhancing Large Vision-Language Models for Complex Visual-Guided Creative Content Generation via Structured Information Extraction](https://mlsanity.com/paper/2508.02890/) (2025)
- [MM-FusionNet: Context-Aware Dynamic Fusion for Multi-modal Fake News Detection with Large Vision-Language Models](https://mlsanity.com/paper/2508.05684/) (2025)
- [ContextGuard-LVLM: Enhancing News Veracity through Fine-grained Cross-modal Contextual Consistency Verification](https://mlsanity.com/paper/2508.06623/) (2025)
- [An Extensive Benchmark for Single-round and Multi-round Instruction-based Image Editing](https://mlsanity.com/paper/2606.15570/) (2026)

## Abstract (arXiv)

This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning. Current MMLMs show impressive zero-shot abilities in multi-modal tasks, but their performance depends heavily on the quality of instructions. VisLingInstruct tackles this by autonomously evaluating and optimizing instructional texts through In-Context Learning, improving the synergy between visual perception and linguistic expression in MMLMs. Alongside this instructional advancement, we have also optimized the visual feature extraction modules in MMLMs, further augmenting their responsiveness to textual content. Our comprehensive experiments on MMLMs, based on FlanT5 and Vicuna, show that VisLingInstruct significantly improves zero-shot performance in visual multi-modal tasks. Notably, it achieves a 13.1% and 9% increase in accuracy over the prior state-of-the-art on the TextVQA and HatefulMemes datasets. Our main code is available at https://github.com/Zhudongsheng75/VisLingInstruct.