The Natural Language Generation (NLG) field stands on the intersection of linguistics and artificial intelligence. It focuses on the creation of human-like text by machines. Recent advancements in Large Language Models (LLMs) have revolutionized NLG, significantly enhancing the flexibility of systems to generate coherent and contextually relevant text. This evolving field necessitates robust evaluation methodologies to evaluate the standard of the generated content accurately.

The central challenge in NLG is ensuring that the generated text not only mimics human language in fluency and grammar but in addition aligns with the intended message and context. Traditional evaluation metrics like BLEU and ROUGE primarily assess surface-level text differences, falling short in evaluating semantic points. This limitation hinders progress in the sector and may result in misleading research conclusions. The emerging use of LLMs for evaluation guarantees a more nuanced and human-aligned assessment, addressing the necessity for more comprehensive methods.  

The researchers from WICT Peking University, Institute of Information Engineering CAS, UTS, Microsoft, and UCLA present a comprehensive study that may be broken into five sections:

1. Introduction
2. Formalization and Taxonomy
3. Generative Evaluation
4. Benchmarks and Tasks
5. Open Problems

1. Introduction:

The introduction sets the stage for the survey by presenting the importance of NLG in AI-driven communication. It highlights the evolution brought by LLMs like GPT-3 in generating text across various applications. The introduction stresses the necessity for robust evaluation methodologies to gauge generated content’s quality accurately. It critiques traditional NLG evaluation metrics for his or her limitations in assessing semantic points and the emergence of LLMs as a promising solution for a more nuanced evaluation.

2. Formalization and Taxonomy:

This survey provides a formalization of LLM-based NLG Evaluation tasks. It outlines a framework for assessing candidate generations across dimensions like fluency and consistency. The taxonomy categorizes NLG evaluation into dimensions: evaluation task, evaluation references, and evaluation function. Each dimension addresses various points of NLG tasks, offering insights into their strengths and limitations in distinct contexts. The approach classifies tasks like Machine Translation, Text Summarization, Dialogue Generation, Story Generation, Image Captioning, Data-to-Text generation, and General Generation.

3. Generative Evaluation:

The study explores the high-capacity generative abilities of LLMs in evaluating NLG text, distinguishing between prompt-based and tuning-based evaluations. It discusses different scoring protocols, including score-based, probability-based, Likert-style, pairwise comparison, ensemble, and advanced evaluation methods. The study provides an in depth exploration of those evaluation methods, accompanied by their respective evaluation protocols, and the way they cater to diverse evaluation needs in NLG.

4. Benchmarks and Tasks:

This study presents a comprehensive overview of varied NLG tasks and the meta-evaluation benchmarks used to validate the effectiveness of LLM-based evaluators. It discusses benchmarks in Machine Translation, Text Summarizing, Dialogue Generation, Image Caption, Data-to-Text, Story Generation, and General Generation. It provides insights into how these benchmarks assess the concurrence between automatic evaluators and human preferences.

5. Open Problems:

The research addresses the unresolved challenges in the sector. It discusses the biases inherent in LLM-based evaluators, the robustness problems with these evaluators, and the complexities surrounding domain-specific evaluation. The study emphasizes the necessity for more flexible and comprehensive evaluation methods able to adapting to complex instructions and real-world requirements, highlighting the gap between current evaluation methods and the evolving capabilities of LLMs.

In conclusion, The survey of LLM-based methods for NLG evaluation highlights a major shift in assessing generated content. These methods offer a more sophisticated and human-aligned approach, addressing the restrictions of traditional evaluation metrics. Using LLMs introduces a nuanced understanding of text quality, encompassing semantic coherence and creativity. This advancement marks a pivotal step towards more accurate and comprehensive evaluations in NLG, promising to boost the reliability and effectiveness of those systems in real-world applications.

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