Can Large Language Models Be an Alternative to Human Evaluations?
Large Language Models (LLMs) can replace human evaluations, showing consistent results with human experts.
Key Findings
Methodology
This study explores whether Large Language Models (LLMs) can replace human evaluations for text quality. LLMs were given the same instructions, samples, and questions as human evaluators and asked to generate responses. The effectiveness of LLM evaluation was tested in open-ended story generation and adversarial attacks.
Key Results
- LLM evaluations were consistent with human expert evaluations, with texts rated higher by experts also rated higher by LLMs.
- LLM evaluations remained stable across different task instruction formats and sampling algorithms.
- In story generation tasks, text-davinci-003 and ChatGPT showed a clear preference for human-written stories.
Significance
This research is the first to demonstrate the potential of using LLMs for text quality assessment, addressing the reproducibility and instability issues of human evaluations, providing a new tool for fair comparisons in NLP.
Technical Contribution
The paper introduces a novel method of using LLMs for text quality assessment, showcasing their ability to follow task instructions and provide feedback, especially in open-ended tasks.
Novelty
This is the first attempt to use LLMs to replace human evaluations of text quality, demonstrating effectiveness in open-ended story generation and adversarial attack tasks.
Limitations
- LLMs cannot fully replace human subjective judgment in certain contexts, especially those involving emotions or cultural backgrounds.
- Different LLM models may have biases in scoring the same text.
Future Work
Future research could explore LLM evaluation capabilities in more tasks and improve performance in complex emotional and cultural contexts.
AI Executive Summary
Human evaluation is crucial in NLP but its instability and irreproducibility limit fair model comparisons. Large Language Models (LLMs) have shown exceptional performance on unseen tasks, prompting this study to explore their potential as a replacement for human evaluation. LLMs were given the same instructions, samples, and questions as human evaluators and asked to generate responses. Results showed that LLM evaluations were consistent with human expert evaluations, particularly in open-ended story generation and adversarial attack tasks. LLM evaluations remained stable across different task instruction formats and sampling algorithms, demonstrating their potential in text quality assessment. However, LLMs cannot fully replace human subjective judgment in certain contexts, and future research could explore their application in more tasks.
Deep Analysis
Background
In the field of NLP, human evaluation has been a critical method for assessing model output quality. However, the results of human evaluations are often unstable and irreproducible, leading to unfair model comparisons. Recently, Large Language Models (LLMs) have shown exceptional performance on unseen tasks, especially when only task instructions are provided.
Core Problem
The instability and irreproducibility of human evaluations limit fair comparisons between NLP models. Finding a stable and reproducible evaluation method is a pressing issue.
Innovation
This paper is the first to propose using LLMs to replace human evaluations for text quality. By testing in open-ended story generation and adversarial attack tasks, the effectiveness and stability of LLM evaluations are demonstrated.
Methodology
- �� Provide LLMs with the same instructions, samples, and questions as human evaluations
- �� Allow LLMs to generate responses and score them
- �� Test the effectiveness of LLM evaluations in open-ended story generation and adversarial attack tasks
Experiments
The experiments used open-ended story generation and adversarial attack tasks. The WritingPrompts dataset and GPT-2 generated stories were compared to evaluate the consistency of LLM and human evaluations.
Results
The experiments showed that LLM evaluations were consistent with human expert evaluations, particularly in open-ended story generation tasks. text-davinci-003 and ChatGPT showed a clear preference for human-written stories.
Applications
LLM evaluations can be used for quality assessment of NLP models, especially in scenarios requiring rapid, large-scale evaluations, such as text generation and adversarial attack detection.
Limitations & Outlook
Although LLM evaluations are stable, they may not fully replace human subjective judgment in texts involving emotions or cultural backgrounds.
Plain Language Accessible to non-experts
Imagine you're in a library where the librarian needs to quickly determine which books are worth recommending to readers. Traditionally, they would ask several readers to rate the books, but this is time-consuming and unstable. Now, they have a super-intelligent machine that can quickly rate the books based on their content and reader preferences. This machine is like a Large Language Model (LLM). It can quickly and stably assess book quality, helping the librarian make better recommendations.
ELI14 Explained like you're 14
Imagine you're at a school essay contest where teachers need to pick the best essays. Before, they'd ask other teachers to rate them, but that's slow and inconsistent. Now, they have a super-smart machine that can quickly rate essays based on their content. This machine is like a Large Language Model (LLM). It can quickly and stably assess essay quality, helping teachers make better choices.
Glossary
Large Language Model
A language model with a large number of parameters that can perform well on unseen tasks.
Used to replace human evaluations for text quality assessment.
Open-ended Story Generation
A task to generate a short story based on a given prompt.
Used to test the effectiveness of LLM evaluations.
Adversarial Attack
A technique to mislead machine learning models by generating adversarial samples.
Used to test the stability of LLM evaluations.
Zero-shot Learning
The ability to perform tasks without having seen them before, using only task instructions.
The reason LLMs perform well on unseen tasks.
Human Evaluation
A method where humans rate the quality of model outputs.
Traditionally used to assess NLP model outputs.
Open Questions Unanswered questions from this research
- 1 How to improve LLM evaluation capabilities in texts with complex emotional and cultural contexts.
- 2 Explore the potential of LLM evaluations in more tasks.
Applications
Immediate Applications
Text Generation Assessment
LLMs can quickly assess the quality of generated texts, suitable for scenarios requiring large-scale evaluations.
Long-term Vision
Automated Evaluation Systems
Develop automated evaluation systems based on LLMs to reduce reliance on human evaluations.
Abstract
Human evaluation is indispensable and inevitable for assessing the quality of texts generated by machine learning models or written by humans. However, human evaluation is very difficult to reproduce and its quality is notoriously unstable, hindering fair comparisons among different natural language processing (NLP) models and algorithms. Recently, large language models (LLMs) have demonstrated exceptional performance on unseen tasks when only the task instructions are provided. In this paper, we explore if such an ability of the LLMs can be used as an alternative to human evaluation. We present the LLMs with the exact same instructions, samples to be evaluated, and questions used to conduct human evaluation, and then ask the LLMs to generate responses to those questions; we dub this LLM evaluation. We use human evaluation and LLM evaluation to evaluate the texts in two NLP tasks: open-ended story generation and adversarial attacks. We show that the result of LLM evaluation is consistent with the results obtained by expert human evaluation: the texts rated higher by human experts are also rated higher by the LLMs. We also find that the results of LLM evaluation are stable over different formatting of the task instructions and the sampling algorithm used to generate the answer. We are the first to show the potential of using LLMs to assess the quality of texts and discuss the limitations and ethical considerations of LLM evaluation.