insight - Text Summarization - # Hallucination mitigation in large language models

Mitigating Hallucinations in Large Language Models through Diversity-Aware Active Learning for Text Summarization

Q: How can the proposed active learning framework be extended to other natural language generation tasks beyond text summarization?

The proposed active learning framework for mitigating hallucinations in large language models (LLMs) can be extended to various other natural language generation tasks beyond text summarization by adapting the hallucination detection metrics and sample selection criteria to suit the specific requirements of each task. For tasks like machine translation, dialogue generation, question answering, and sentiment analysis, different types of hallucinations may occur, such as mistranslations, incorrect responses, or biased sentiment generation. By incorporating task-specific hallucination detection models and defining diverse hallucination types relevant to each task, the active learning framework can effectively identify and address hallucinations in LLM outputs. Additionally, the sample selection strategy can be tailored to prioritize samples that are more likely to exhibit hallucinations specific to the task at hand, ensuring a comprehensive mitigation approach across various natural language generation tasks.

Q: What are the potential biases that may be introduced by the hallucination diversity-aware sample selection strategy, and how can they be mitigated?

The hallucination diversity-aware sample selection strategy may introduce biases in the selection of samples for annotation, leading to imbalanced coverage of different types of hallucinations. One potential bias is the overrepresentation of certain types of hallucinations if the diversity measure is not appropriately weighted or if certain types are more easily detected by the hallucination detection models. This bias can result in the neglect of other types of hallucinations, leading to incomplete mitigation efforts. To mitigate these biases, it is essential to regularly evaluate the performance of the hallucination detection models and adjust the weights assigned to different types of hallucinations based on their detection accuracy and prevalence in the LLM outputs. Additionally, incorporating feedback mechanisms that monitor the distribution of selected samples and the effectiveness of the mitigation strategy can help identify and correct biases in the sample selection process. By continuously refining the diversity-aware sample selection strategy based on empirical results and feedback, biases can be minimized, ensuring a more balanced and comprehensive approach to hallucination mitigation.

Q: Given the challenges in obtaining human annotations for hallucinations, how can the active learning framework be further improved to minimize the need for human involvement?

To minimize the need for human annotations in the active learning framework for hallucination mitigation, several strategies can be implemented: Semi-supervised Learning: Incorporate semi-supervised learning techniques to leverage both labeled and unlabeled data for model training. By utilizing the vast amount of unlabeled data available, the model can learn from the inherent patterns in the data and reduce the reliance on human annotations. Self-supervised Learning: Integrate self-supervised learning methods that enable the model to generate its own annotations or labels during training. By designing self-supervised tasks that encourage the model to identify and correct hallucinations in its outputs, human involvement can be minimized. Active Learning with Simulation: Develop simulation environments or synthetic data generation techniques to simulate hallucinations and train the model to detect and mitigate them. This approach can reduce the need for real human annotations while still providing valuable training data. Transfer Learning: Utilize transfer learning from pre-trained models that have been fine-tuned on similar tasks or datasets with annotated hallucinations. By transferring knowledge from these models, the need for extensive human annotations can be reduced, accelerating the learning process. By incorporating these strategies and exploring innovative approaches to reduce human involvement, the active learning framework can be further improved to effectively mitigate hallucinations in LLMs while minimizing the need for labor-intensive human annotations.

Core Concepts

This paper proposes an active learning framework to effectively and efficiently mitigate hallucinations in large language models (LLMs) for text summarization by selecting diverse hallucination samples for annotation and finetuning.

Abstract

The paper addresses the problem of hallucinations in large language models (LLMs) for text summarization. Hallucinations refer to the generation of seemingly plausible but factually incorrect or unsupported outputs by LLMs.

The authors first revisit the typology of hallucinations in text summarization, identifying three main types: semantic frame errors, discourse errors, and content verifiability errors. They then propose an active learning framework to mitigate these hallucinations in LLMs, reducing the need for costly human annotations.

The key components of the framework are:

Capturing diverse hallucination types: The authors leverage existing detection models to measure semantic frame, discourse, and content verifiability errors in the LLM-generated summaries.
Hallucination diversity-aware sample selection: The authors propose a sample selection strategy called HADAS that not only selects samples with low hallucination scores but also ensures diversity in the types of hallucinations exhibited.
Iterative finetuning of LLMs: The selected and annotated samples are used to finetune the LLMs, with the goal of comprehensively mitigating hallucinations.

Extensive experiments on three datasets and different backbone LLMs demonstrate the effectiveness of the proposed HADAS method in alleviating hallucinations while maintaining high summarization quality, outperforming both random sampling and existing diversity-based approaches.

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About 300,000 people are still trapped by the worst flooding in the region for 50 years.
The state of Tabasco in southern Mexico has experienced heavy rains and flooding that have forced hundreds of thousands of people from their homes over the past four days.

Quotes

"Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported."
"Existing methods for hallucination mitigation often focus on finetuning LLMs with human feedback or human-annotated samples to align the models' outputs with human-plausible content."

Key Insights Distilled From

Hallucination Diversity-Aware Active Learning for Text Summarization

by Yu Xia,Xu Li... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01588.pdf

Hallucination Diversity-Aware Active Learning for Text Summarization

Deeper Inquiries

How can the proposed active learning framework be extended to other natural language generation tasks beyond text summarization?

The proposed active learning framework for mitigating hallucinations in large language models (LLMs) can be extended to various other natural language generation tasks beyond text summarization by adapting the hallucination detection metrics and sample selection criteria to suit the specific requirements of each task. For tasks like machine translation, dialogue generation, question answering, and sentiment analysis, different types of hallucinations may occur, such as mistranslations, incorrect responses, or biased sentiment generation. By incorporating task-specific hallucination detection models and defining diverse hallucination types relevant to each task, the active learning framework can effectively identify and address hallucinations in LLM outputs. Additionally, the sample selection strategy can be tailored to prioritize samples that are more likely to exhibit hallucinations specific to the task at hand, ensuring a comprehensive mitigation approach across various natural language generation tasks.

What are the potential biases that may be introduced by the hallucination diversity-aware sample selection strategy, and how can they be mitigated?

The hallucination diversity-aware sample selection strategy may introduce biases in the selection of samples for annotation, leading to imbalanced coverage of different types of hallucinations. One potential bias is the overrepresentation of certain types of hallucinations if the diversity measure is not appropriately weighted or if certain types are more easily detected by the hallucination detection models. This bias can result in the neglect of other types of hallucinations, leading to incomplete mitigation efforts.
To mitigate these biases, it is essential to regularly evaluate the performance of the hallucination detection models and adjust the weights assigned to different types of hallucinations based on their detection accuracy and prevalence in the LLM outputs. Additionally, incorporating feedback mechanisms that monitor the distribution of selected samples and the effectiveness of the mitigation strategy can help identify and correct biases in the sample selection process. By continuously refining the diversity-aware sample selection strategy based on empirical results and feedback, biases can be minimized, ensuring a more balanced and comprehensive approach to hallucination mitigation.

Given the challenges in obtaining human annotations for hallucinations, how can the active learning framework be further improved to minimize the need for human involvement?

To minimize the need for human annotations in the active learning framework for hallucination mitigation, several strategies can be implemented:

Semi-supervised Learning: Incorporate semi-supervised learning techniques to leverage both labeled and unlabeled data for model training. By utilizing the vast amount of unlabeled data available, the model can learn from the inherent patterns in the data and reduce the reliance on human annotations.

Self-supervised Learning: Integrate self-supervised learning methods that enable the model to generate its own annotations or labels during training. By designing self-supervised tasks that encourage the model to identify and correct hallucinations in its outputs, human involvement can be minimized.

Active Learning with Simulation: Develop simulation environments or synthetic data generation techniques to simulate hallucinations and train the model to detect and mitigate them. This approach can reduce the need for real human annotations while still providing valuable training data.

Transfer Learning: Utilize transfer learning from pre-trained models that have been fine-tuned on similar tasks or datasets with annotated hallucinations. By transferring knowledge from these models, the need for extensive human annotations can be reduced, accelerating the learning process.

By incorporating these strategies and exploring innovative approaches to reduce human involvement, the active learning framework can be further improved to effectively mitigate hallucinations in LLMs while minimizing the need for labor-intensive human annotations.