# CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning

arXiv: https://arxiv.org/abs/2309.02301 · HTML: https://mlsanity.com/paper/2309.02301/

## Key facts

- Cited by 69 papers in the map (data through 14 Oct 2026).
- Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 28 of its 28 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Hallucination mitigation in LVLMs](https://mlsanity.com/mllm/hallucination-mitigation-in-lvlms/)

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [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)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [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)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models](https://mlsanity.com/paper/2306.13394/) (2023)
- [Object Hallucination in Image Captioning](https://mlsanity.com/paper/1809.02156/) (2018)
- [Otter: A Multi-Modal Model with In-Context Instruction Tuning](https://mlsanity.com/paper/2305.03726/) (2023)
- [nocaps: novel object captioning at scale](https://mlsanity.com/paper/1812.08658/) (2018)
- [LAMM: Language-Assisted Multi-Modal Instruction-Tuning Dataset, Framework, and Benchmark](https://mlsanity.com/paper/2306.06687/) (2023)
- [From Show to Tell: A Survey on Deep Learning-based Image Captioning](https://mlsanity.com/paper/2107.06912/) (2021)
- [Vision-Language Pre-training: Basics, Recent Advances, and Future Trends](https://mlsanity.com/paper/2210.09263/) (2022)

## Built on by (most cited first, 30 of 69)

- [Hallucination of Multimodal Large Language Models: A Survey](https://mlsanity.com/paper/2404.18930/) (2024)
- [A Survey on Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2402.00253/) (2024)
- [Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs](https://mlsanity.com/paper/2407.21771/) (2024)
- [A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models](https://mlsanity.com/paper/2405.09589/) (2024)
- [Multi-Object Hallucination in Vision-Language Models](https://mlsanity.com/paper/2407.06192/) (2024)
- [AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models](https://mlsanity.com/paper/2410.18325/) (2024)
- [CLIP-DPO: Vision-Language Models as a Source of Preference for Fixing Hallucinations in LVLMs](https://mlsanity.com/paper/2408.10433/) (2024)
- [PhD: A ChatGPT-Prompted Visual hallucination Evaluation Dataset](https://mlsanity.com/paper/2403.11116/) (2024)
- [Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding](https://mlsanity.com/paper/2505.16652/) (2025)
- [Evaluating and Analyzing Relationship Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2406.16449/) (2024)
- [Large Vision-Language Model Alignment and Misalignment: A Survey Through the Lens of Explainability](https://mlsanity.com/paper/2501.01346/) (2025)
- [Hal-Eval: A Universal and Fine-grained Hallucination Evaluation Framework for Large Vision Language Models](https://mlsanity.com/paper/2402.15721/) (2024)
- [A Survey of Multimodal Hallucination Evaluation and Detection](https://mlsanity.com/paper/2507.19024/) (2025)
- [Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Models](https://mlsanity.com/paper/2404.18746/) (2024)
- [SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding](https://mlsanity.com/paper/2506.08391/) (2025)
- [BEAF: Observing BEfore-AFter Changes to Evaluate Hallucination in Vision-language Models](https://mlsanity.com/paper/2407.13442/) (2024)
- [Vision-Language Instruction Tuning: A Review and Analysis](https://mlsanity.com/paper/2311.08172/) (2023)
- [Pelican: Correcting Hallucination in Vision-LLMs via Claim Decomposition and Program of Thought Verification](https://mlsanity.com/paper/2407.02352/) (2024)
- [Multimodal Large Language Models for Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2504.21051/) (2025)
- [ARGUS: Hallucination and Omission Evaluation in Video-LLMs](https://mlsanity.com/paper/2506.07371/) (2025)
- [Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models](https://mlsanity.com/paper/2410.03176/) (2024)
- [Reflective Instruction Tuning: Mitigating Hallucinations in Large Vision-Language Models](https://mlsanity.com/paper/2407.11422/) (2024)
- [DHCP: Detecting Hallucinations by Cross-modal Attention Pattern in Large Vision-Language Models](https://mlsanity.com/paper/2411.18659/) (2024)
- [MoHoBench: Assessing Honesty of Multimodal Large Language Models via Unanswerable Visual Questions](https://mlsanity.com/paper/2507.21503/) (2025)
- [Mitigating Hallucinations in Large Vision-Language Models via Entity-Centric Multimodal Preference Optimization](https://mlsanity.com/paper/2506.04039/) (2025)
- [Inverse IFEval: Can LLMs Unlearn Stubborn Training Conventions to Follow Real Instructions?](https://mlsanity.com/paper/2509.04292/) (2025)
- [Does Object Grounding Really Reduce Hallucination of Large Vision-Language Models?](https://mlsanity.com/paper/2406.14492/) (2024)
- [Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models](https://mlsanity.com/paper/2505.07001/) (2025)
- [Hallucination in Multimodal Foundation Models: A Survey on Causes, Corrections, and Evaluations](https://mlsanity.com/paper/2410.15359/) (2024)
- [LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models](https://mlsanity.com/paper/2410.09962/) (2024)

## Abstract (arXiv)

Nowadays, the research on Large Vision-Language Models (LVLMs) has been significantly promoted thanks to the success of Large Language Models (LLM). Nevertheless, these Vision-Language Models (VLMs) are suffering from the drawback of hallucination -- due to insufficient understanding of vision and language modalities, VLMs may generate incorrect perception information when doing downstream applications, for example, captioning a non-existent entity. To address the hallucination phenomenon, on the one hand, we introduce a Contrastive Instruction Evaluation Method (CIEM), which is an automatic pipeline that leverages an annotated image-text dataset coupled with an LLM to generate factual/contrastive question-answer pairs for the evaluation of the hallucination of VLMs. On the other hand, based on CIEM, we further propose a new instruction tuning method called CIT (the abbreviation of Contrastive Instruction Tuning) to alleviate the hallucination of VLMs by automatically producing high-quality factual/contrastive question-answer pairs and corresponding justifications for model tuning. Through extensive experiments on CIEM and CIT, we pinpoint the hallucination issues commonly present in existing VLMs, the disability of the current instruction-tuning dataset to handle the hallucination phenomenon and the superiority of CIT-tuned VLMs over both CIEM and public datasets.