Leveraging Large Language Models to Diversify References and Improve Natural Language Generation Evaluation

Core Concepts

Enriching the number of references in NLG benchmarks can significantly enhance the correlation between automatic evaluation metrics and human judgments.

Abstract

The paper presents a simple and effective method, named Div-Ref, to enhance existing NLG evaluation benchmarks by leveraging large language models (LLMs) to diversify the expression of a single reference into multiple high-quality ones. This aims to cover the semantic space of the reference sentence as much as possible. The key highlights are: The authors formulate the NLG evaluation problem and identify the limitation of using a single or few references, which may result in poor correlations with human judgments. The Div-Ref method utilizes LLMs to generate diverse expressions of a single reference, creating multiple semantically equivalent references. This expands the coverage of the semantic space for evaluating generated texts. Extensive experiments on multiple NLG benchmarks, including machine translation, text summarization, and image captioning, demonstrate that diversifying the references can significantly enhance the correlation between automatic evaluation and human evaluation. The authors show that their approach is compatible with recent LLM-based evaluation metrics, enabling them to benefit from the diversified references and achieve state-of-the-art correlation with human judges. The authors strongly encourage future generation benchmarks to include more references, even if they are generated by LLMs, as it is a one-time effort that can benefit future research.

Stats

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Quotes

"Enriching the number of references in NLG benchmarks can significantly enhance the correlation between automatic evaluation metrics and human judgments." "The Div-Ref method utilizes LLMs to generate diverse expressions of a single reference, creating multiple semantically equivalent references. This expands the coverage of the semantic space for evaluating generated texts." "The authors strongly encourage future generation benchmarks to include more references, even if they are generated by LLMs, as it is a one-time effort that can benefit future research."

Key Insights Distilled From

Not All Metrics Are Guilty

by Tianyi Tang,... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2305.15067.pdf

Deeper Inquiries

How can the diversifying method be further improved to handle specialized domains with technical terminology?

In order to enhance the diversifying method to handle specialized domains with technical terminology, several strategies can be implemented: Domain-specific prompts: Tailoring the prompts used for diversifying references to include domain-specific vocabulary and terminology can help ensure that the generated references are contextually relevant and accurate within specialized domains. Fine-tuning on domain-specific data: Training the LLMs on domain-specific data can improve their understanding and generation of technical terms and language specific to specialized domains. This fine-tuning process can help the models produce more relevant and accurate diversified references. Human validation: Incorporating human validation or feedback mechanisms to verify the accuracy and relevance of the generated references in specialized domains can help ensure the quality of the diversified references. Human annotators can provide domain expertise to validate the suitability of the generated references. Hybrid approaches: Combining the strengths of LLMs with domain-specific rule-based systems or terminology databases can enhance the diversifying method's ability to handle technical terminology. By leveraging both automated generation and curated domain knowledge, the method can produce more precise and domain-specific references.

How can the potential drawbacks or limitations of relying on LLMs to generate diverse references be addressed?

While LLMs offer significant capabilities for generating diverse references, there are potential drawbacks and limitations that need to be addressed: Bias and inaccuracies: LLMs may exhibit biases or inaccuracies in their generated references, leading to suboptimal results. Addressing this limitation requires continuous monitoring, evaluation, and fine-tuning of the models to reduce bias and improve accuracy. Lack of domain expertise: LLMs may lack domain-specific knowledge and context, resulting in inaccuracies when generating references in specialized domains. Incorporating domain-specific data and fine-tuning the models on domain-specific information can help mitigate this limitation. Scalability and efficiency: Generating multiple diverse references using LLMs can be computationally intensive and time-consuming, impacting scalability and efficiency. Optimizing the generation process, leveraging parallel computing resources, and implementing efficient algorithms can address these challenges. Evaluation and validation: Ensuring the quality and relevance of the generated references from LLMs requires robust evaluation and validation mechanisms. Implementing human validation, automated quality checks, and post-generation filtering can help address this limitation.

How can the trade-off between the number of references and the evaluation time be optimized to achieve a practical and efficient solution?

To optimize the trade-off between the number of references and evaluation time for a practical and efficient solution, the following strategies can be implemented: Selective reference generation: Instead of generating a large number of references for every sample, a selective approach can be adopted where only the most diverse and relevant references are generated. This selective generation can help reduce evaluation time while maintaining the quality of the references. Parallel processing: Utilizing parallel processing techniques and distributed computing resources can speed up the reference generation process, allowing for the generation of multiple references in a more efficient manner. This can help optimize evaluation time without compromising on the number of references. Dynamic reference selection: Implementing a dynamic reference selection mechanism that adapts based on the complexity of the sample or the performance of the model can help optimize the trade-off. This approach can prioritize generating additional references for challenging samples while reducing the number for simpler ones. Incremental evaluation: Conducting incremental evaluation where references are generated and evaluated iteratively can help optimize evaluation time. This approach allows for real-time feedback and adjustment of the reference generation process based on the evaluation results, leading to a more efficient solution.