Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning: A Comprehensive Study

Core Concepts

Addressing hallucination in large multi-modal models through robust instruction tuning.

Abstract

This content discusses the challenges of hallucination in large multi-modal models and introduces a new dataset, LRV-Instruction, to mitigate this issue. The study explores the impact of negative instructions on model performance and proposes GAVIE for evaluation. Experiments show improved model performance with balanced training data ratios.

Directory:

1. Introduction
   • Progress in natural language processing and multi-modal models.
2. Data Extraction
   • Introduction of LRV-Instruction dataset.
   • Negative instructions designed for robust visual instruction tuning.
3. Evaluation Methodology
   • GPT4-Assisted Visual Instruction Evaluation (GAVIE).
4. Experiment Results
   • Improved model performance with LRV-Instruction.
5. Detailed Analysis
   • Performance at different semantic levels of hallucination.
6. Conclusion and Future Directions

Stats

Our dataset comprises 400k visual instructions generated by GPT4.
Existing LMMs exhibit significant hallucinations with negative instructions.
Finetuning on LRV-Instruction improves model performance compared to state-of-the-art methods.

Quotes

"Despite promising progress in multi-modal tasks, current large multi-modal models are prone to hallucinating inconsistent descriptions."
"Our investigation reveals that most LMMs are finetuned on unbalanced datasets containing only positive instructions."
"We hope our work can help address the unexpected hallucination issues of LMMs."

Key Insights Distilled From

Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

by Fuxiao Liu,K... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2306.14565.pdf

Deeper Inquiries

How can negative instructions improve the robustness of large multi-modal models?

Negative instructions play a crucial role in enhancing the robustness of large multi-modal models by providing them with a more comprehensive understanding of what not to do or include in their responses. Here are some ways in which negative instructions contribute to improving model performance:

Addressing Hallucination: Negative instructions help prevent hallucinations by guiding the model on what elements should not be present in its output. By including scenarios where non-existent objects, incorrect attributes, or misleading information are introduced, the model learns to avoid generating inaccurate descriptions.

Balanced Training Data: Including both positive and negative instances ensures that the model is exposed to a diverse range of examples during training. This balanced dataset helps prevent bias towards generating only affirmative responses and encourages the model to provide accurate answers even when faced with challenging or deceptive instructions.

Fine-tuning for Faithful Responses: Negative instructions challenge the model's ability to follow human directions accurately, leading to improved instruction tuning capabilities. Models trained on datasets containing negative samples exhibit better adherence to given tasks and are less likely to deviate from provided guidelines.

Enhancing Task Understanding: By incorporating negative examples that test different semantic levels such as Nonexistent Object Manipulation, Existent Object Manipulation, and Knowledge Manipulation, models gain a deeper comprehension of task requirements and develop more nuanced decision-making abilities.

In summary, integrating negative instructions into training data fosters a more resilient and reliable behavior in large multi-modal models by guiding them towards producing faithful responses aligned with human expectations.

What are the implications of relying on synthetic instruction data for training?

Relying solely on synthetic instruction data for training large multi-modal models can have several implications that may impact their performance and generalization capabilities:

Limited Diversity: Synthetic instruction data often lacks diversity compared to real-world inputs, potentially leading to overfitting on specific patterns present in the generated data but absent in actual user interactions or tasks.

Hallucination Risk: Models trained on synthetic data may exhibit higher tendencies towards hallucination—generating outputs that contain false information or details not supported by input modalities due to exposure primarily limited within synthesized contexts.

Generalization Challenges: Synthetic datasets may not fully capture all nuances present in natural language interactions or visual cues encountered during inference tasks outside controlled environments; this limitation could hinder how well models adapt across various domains or unforeseen scenarios.

Bias Amplification: If synthetic datasets inadvertently introduce biases during generation processes (e.g., biased language priors), these biases can get amplified through training cycles without counterbalancing influences from real-world variations found in authentic instructional content.

5 .Evaluation Misalignment: Models fine-tuned solely on synthetic instruction sets might perform well internally but struggle when faced with real-world applications due t...

How can GAVIE be adapted to evaluate other aspects of model performance beyond hallucination?

GPT4-Assisted Visual Instruction Evaluation (GAVIE) offers flexibility for assessing various aspects of model performance beyond just hallucination within multimodal systems like Large Multi-Modal Models (LMMs). Here’s how GAVIE can be adapted for broader evaluation purposes:
1 .Task Completion Accuracy: Modify GAVIE prompts and criteria based on task completion accuracy rather than focusing solely on hallucination detection.
2 .Semantic Consistency: Expand GAVIE metrics ...
3 .Contextual Relevance: Adjust GAVIE scoring mechanisms ...
4 .Language Fluency: Incorporate additional evaluation criteria within GAVIE focused specifically ...
5 .**Real-time Feedback Mechanism: Enhance GAVIE functionality ...