Leveraging Large Language Models for NLG Evaluation: A Survey (AI summary)

Key Points

1. Large Language Models (LLMs) have revolutionized the capabilities of Natural Language Generation (NLG) systems, leading to the need for robust evaluation methodologies to assess the quality of the generated content.

2. Traditional NLG evaluation metrics are limited in assessing semantic aspects and tend to have low alignment with human judgment, underscoring the need for more nuanced and comprehensive evaluation methods in the NLG field.

3. The emergent abilities of LLMs offer promising ways to evaluate NLG outputs, providing a more sophisticated and human-aligned assessment compared to traditional methods.

4. A coherent taxonomy classifies LLM-based approaches along three primary dimensions: evaluation task, evaluation references, and evaluation function, enabling a systematic categorization and understanding of these methodologies.

5. Challenging areas in LLM-based NLG evaluation include bias, robustness, domain-specific evaluation, and the need for more unified and comprehensive evaluation techniques within LLM-based evaluators.

6. The use of LLM-based evaluators needs to address the inherent biases, robustness, and potential egocentric biases of the models.

7. There are challenges in leveraging LLMs for evaluation purposes, such as the reliance on more powerful LLMs for evaluation and the lack of domain-specific knowledge for specific tasks.

8. The wide-ranging capabilities of LLMs and the increasing complexity of user queries call for the development of more unified and contemporary evaluation protocols for NLG systems.

9. Advancements in NLG evaluation using LLMs are crucial in paving the way for more general, effective, and reliable NLG evaluation techniques, which will contribute significantly to the progression of the field.

Summary

The research paper proposes a comprehensive framework for evaluating Natural Language Generation (NLG) outputs based on the use of Large Language Models (LLMs). The paper introduces a taxonomy for categorizing NLG evaluation approaches, focusing on generative-based methods. It discusses the limitations of traditional NLG evaluation metrics and highlights the emerging potential of large language models for NLG evaluation, particularly in context comprehension and generating reasonable responses.

Additionally, the paper explores the challenges and potential avenues for future scholarly exploration in the field of NLG evaluation, emphasizing the need for more nuanced and comprehensive evaluation methods. The paper also provides a detailed overview of recent advancements in leveraging LLMs for NLG evaluation, discussing different tasks, evaluation references, and evaluation functions. The authors suggest that addressing these challenges will contribute to more effective and reliable NLG evaluation techniques, paving the way for the broader application of LLMs.

Reference: https://arxiv.org/abs/2401.07103