Overview : "Mitigating Hallucination in Large Language Models: Reasons and Current Research"

Introduction

Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG), offering unprecedented levels of fluency and coherence in generated text. However, a significant challenge arises when these models produce linguistically fluent but semantically inaccurate outputs, a phenomenon known as hallucination. This article aims to explain the reasons behind LLM hallucination and the current research being conducted to mitigate this issue.

Reasons for LLM Hallucination

LLMs can hallucinate due to their reliance on fluency-centric metrics and the generation of fluent yet inaccurate outputs. This occurs when the model produces text that is linguistically correct but semantically incorrect or irrelevant to the input prompt. The challenge lies in the model's inability to accurately capture the intended meaning or context, leading to the generation of hallucinated content.

Another reason for hallucination is the incorrect labeling of ground truth. Ground truth refers to the correct or expected output of a model. If the ground truth is incorrectly labeled, the model will learn to generate incorrect responses, leading to hallucination.

Current Research on Mitigating Hallucination

Researchers are actively working on methods to mitigate hallucination in LLMs. One approach is the use of data augmentation, which involves creating new training data by modifying existing data. This can help the model learn to generate more accurate and contextually relevant responses.

Another approach is the use of an ensemble of different methodologies. This involves combining multiple models or techniques to improve the overall performance. For instance, a recent study proposed an automatic pipeline for hallucination detection that utilized an ensemble of three different methodologies. This approach achieved an accuracy of 80.07% in a semantic hallucination task.

One of the novel methods in this ensemble is the sequential method, which involves training the model sequentially on different tasks. This method was able to outperform the other two methods due to its ability to learn from a diverse range of tasks.

Data Augmentation Techniques

To enrich the original data available, researchers propose using different augmentation techniques, including LLM-aided pseudo-labeling and sentence rephrasing. LLM-aided pseudo-labeling involves generating synthetic labels for unlabelled data through a few-shot learning approach. Sentence rephrasing, on the other hand, is used to provide the model with diverse data while maintaining the reliability of the labels.

Ensemble Model for Hallucination Detection

The use of an ensemble of three different approaches is suggested to improve the performance of hallucination detection. These approaches include a simple BERT-based classifier, a model trained through Conditioned Reinforcement Learning Fine Tuning (C-RLFT), and a sequential model based on iterative fine-tuning. The ensemble benefits from using different, complementary approaches, particularly in terms of recall.

Conclusion

Hallucination is a significant challenge in the field of natural language processing. While LLMs have made significant strides in generating human-like text, they are still prone to generating nonsensical or factually incorrect responses. However, with ongoing research and the development of new techniques, we are moving closer to mitigating this problem. The use of data augmentation, ensemble methods, and novel techniques like the sequential method are promising avenues for improving the accuracy and reliability of LLMs.