Fine-Tuning Small Language Models on Data from Larger Models Increases Hallucination

Core Concepts

Fine-tuning small language models on data generated by larger models, while seemingly improving performance, actually increases their tendency to hallucinate or generate factually incorrect information.

Abstract

• Bibliographic Information: Wee, P., & Baghdadi, R. (2024). Exploring the Knowledge Mismatch Hypothesis: Hallucination Propensity in Small Models Fine-tuned on Data from Larger Models. arXiv preprint arXiv:2411.00878v1.
• Research Objective: This paper investigates whether fine-tuning small language models on data generated by larger models leads to a "knowledge mismatch" that increases the likelihood of generating factually incorrect information (hallucination).
• Methodology: The researchers fine-tuned a small language model (LLaMA 7B) and a large language model (LLaMA 13B) to answer trivia questions. They then created two fine-tuning datasets: one with answers generated by the small model and one with answers generated by the large model. Finally, they fine-tuned the small model on both datasets and compared their performance on an unseen test set.
• Key Findings: The small model fine-tuned on data generated by the large model produced significantly more wrong answers (an average of 125% increase) compared to the model fine-tuned on data generated by itself.
• Main Conclusions: Fine-tuning small language models on data from larger models can lead to a knowledge mismatch, where the smaller model is trained to provide answers to questions it hasn't truly learned, increasing the likelihood of hallucination.
• Significance: This research highlights a potential pitfall in the common practice of fine-tuning smaller language models using data from larger models. It suggests that this approach, while seemingly effective, can negatively impact the model's factual accuracy.
• Limitations and Future Research: The study was limited to specific LLaMA models and a single trivia dataset. Further research is needed to explore the generalizability of these findings across different models, datasets, and tasks. Additionally, investigating mitigation strategies for this knowledge mismatch could be beneficial.

Stats

The small model fine-tuned on data from the larger model showed an average of 125% increase in wrong answers.
The median increase in wrong answers for the small model fine-tuned on data from the larger model was 107%.

Quotes

"Our analysis finds that on an unseen test set, a smaller model fine-tuned on data generated from a larger model produced more wrong answers when compared to models fine-tuned on data created by the small model, which confirms the hypothesis."
"It is hypothesized that a mismatch occurs when there is a difference between the knowledge fed to the model to fine-tune it and the knowledge that is already present in the model. This mismatch either teaches the model to hallucinate or to withhold information."

Key Insights Distilled From

Exploring the Knowledge Mismatch Hypothesis: Hallucination Propensity in Small Models Fine-tuned on Data from Larger Models

by Phil Wee, Ri... at arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.00878.pdf

Deeper Inquiries

How can we develop fine-tuning techniques that mitigate this knowledge mismatch and reduce hallucination in smaller language models?

Addressing the knowledge mismatch highlighted in the paper requires fine-tuning techniques that go beyond simply adapting to the outputs of larger models. Here are some potential avenues:

Knowledge-Aware Fine-tuning:

Incorporate Knowledge Bases: Instead of relying solely on the larger model's outputs, integrate external knowledge bases (e.g., Wikidata, ConceptNet) during fine-tuning. This can help ground the smaller model's understanding and reduce reliance on potentially inaccurate or incomplete knowledge from the larger model.
Knowledge Distillation with Consistency Constraints: Adapt knowledge distillation techniques to focus on transferring not just the larger model's predictions but also its underlying knowledge representation. Enforce consistency between the smaller model's outputs and the knowledge base during fine-tuning.

Data Augmentation and Filtering:

Targeted Data Augmentation: Augment the fine-tuning data with examples specifically designed to address the knowledge gaps identified in the smaller model. This could involve generating questions that target the smaller model's weaknesses or using techniques like paraphrasing to create diverse examples.
Confidence-Based Data Filtering: Filter the fine-tuning data from the larger model based on its confidence scores. Prioritize examples where the larger model is highly confident, as these are more likely to reflect accurate knowledge.

Hybrid Fine-tuning Approaches:

Curriculum Learning: Start fine-tuning with a simpler dataset generated by the smaller model itself, gradually introducing more complex data from the larger model as the smaller model's knowledge base grows.
Ensemble Methods: Fine-tune multiple smaller models on different subsets of the larger model's data or using different knowledge sources. Combine their predictions during inference to mitigate the impact of individual model biases and hallucinations.

Evaluation and Monitoring:

Hallucination-Specific Metrics: Develop and use evaluation metrics that specifically target hallucination, going beyond traditional accuracy measures. This could involve assessing the factual consistency of generated text or measuring the model's reliance on external knowledge sources.
Continuous Monitoring and Adaptation: Implement mechanisms to continuously monitor the smaller model's performance and identify potential hallucinations in real-world use. Use this feedback to further refine the fine-tuning process and address emerging issues.

By focusing on knowledge transfer, data quality, and robust evaluation, we can develop fine-tuning techniques that produce smaller language models that are not only performant but also less susceptible to hallucination.

Could the increased "correct" answers from the smaller model fine-tuned on larger model data be a result of memorization rather than true understanding, further contributing to the potential for hallucination in different contexts?

Yes, the increase in "correct" answers observed when a smaller model is fine-tuned on data from a larger model could indeed be indicative of memorization rather than genuine understanding. This phenomenon, often referred to as overfitting, occurs when a model learns to associate specific input patterns with corresponding outputs without developing a deeper comprehension of the underlying concepts.
Here's how memorization can manifest and contribute to hallucination:

Surface-Level Pattern Recognition: The smaller model might pick up on superficial cues or patterns in the larger model's outputs, such as specific phrasing or keywords, without grasping the actual meaning or context. This can lead to correct answers on similar-looking questions but incorrect or nonsensical responses when presented with novel or slightly modified prompts.
Data Bias Amplification: If the larger model's training data contains biases or inaccuracies, the smaller model might inadvertently learn and amplify these biases during fine-tuning. This can result in hallucinations that reflect the biases present in the training data, even if the smaller model itself has not been explicitly exposed to such biases.
Lack of Generalization: A model that has memorized patterns is less likely to generalize well to unseen data or different contexts. It might perform well on the specific questions it was fine-tuned on but struggle when faced with questions that require reasoning, inference, or knowledge beyond the memorized patterns.
Addressing Memorization:

Regularization Techniques: Employ regularization techniques during fine-tuning, such as dropout or weight decay, to prevent the smaller model from becoming overly reliant on specific features or patterns in the training data.
Out-of-Distribution Testing: Evaluate the smaller model's performance on data that is significantly different from the fine-tuning data. This helps assess its ability to generalize beyond memorized patterns and identify potential hallucinations in new contexts.
Explainability Techniques: Utilize explainability techniques to understand the reasoning behind the smaller model's predictions. This can help identify instances where the model is relying on memorized patterns rather than true understanding.
By addressing memorization, we can encourage the smaller model to develop a more robust and generalizable understanding of the knowledge it is being fine-tuned on, reducing the likelihood of hallucinations in diverse contexts.

What are the ethical implications of using language models that are prone to hallucination, especially in applications where factual accuracy is crucial, and how can we address these concerns?

The propensity of language models to hallucinate raises significant ethical concerns, particularly when deployed in applications demanding high factual accuracy. Here's a breakdown of the implications and potential mitigation strategies:
Ethical Implications:

Spread of Misinformation: Hallucinations can lead to the generation and propagation of false or misleading information, potentially causing harm in various domains. For instance, in news reporting, it could erode trust and fuel societal discord. In healthcare, inaccurate medical advice could have dire consequences.
Unfair or Biased Outcomes: If hallucinations are influenced by biases present in the training data, they can perpetuate and even amplify existing societal biases. This could result in unfair or discriminatory outcomes in areas like hiring, loan applications, or criminal justice.
Erosion of Trust: As users become aware of the potential for hallucinations, trust in language models and their outputs may diminish. This could hinder the adoption of these technologies, even in applications where they could be beneficial.
Lack of Accountability: Determining responsibility and accountability when a language model generates harmful hallucinations can be challenging. It raises questions about the liability of developers, deployers, and even users in such situations.
Addressing Ethical Concerns:

Transparency and Disclosure: Clearly communicate the limitations of language models, including their potential for hallucination, to users. Provide mechanisms for users to report suspected hallucinations and seek clarification or verification.
Robust Evaluation and Testing: Conduct rigorous evaluation and testing of language models, particularly in domain-specific contexts, to identify and mitigate potential hallucinations. Develop and use evaluation metrics that specifically target factual accuracy and bias.
Human Oversight and Review: Implement human-in-the-loop systems where critical decisions or outputs generated by language models are subject to human review and verification. This is particularly crucial in high-stakes domains like healthcare or finance.
Bias Detection and Mitigation: Develop and employ techniques to detect and mitigate biases in both the training data and the outputs of language models. This includes promoting diversity in training datasets and using fairness-aware algorithms.
Ethical Guidelines and Regulations: Establish clear ethical guidelines and regulations for the development, deployment, and use of language models. This should involve collaboration between researchers, developers, policymakers, and ethicists.
Addressing the ethical implications of language model hallucinations requires a multi-faceted approach that prioritizes transparency, accountability, and user safety. By acknowledging these concerns and implementing appropriate safeguards, we can harness the potential of language models while mitigating the risks they pose.