xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation
xFinder optimizes key answer extraction in LLM evaluation, achieving 93.42% extraction accuracy.
Key Findings
Methodology
xFinder enhances LLM evaluation by optimizing the key answer extraction module. The study introduces the KAF dataset to support model training and evaluation. xFinder employs a 500M parameter model and a self-consistency strategy for efficient answer extraction and matching.
Key Results
- xFinder achieves 93.42% extraction accuracy on the KAF dataset, significantly higher than RegEx's 74.38%.
- Final judgment accuracy reaches 97.61%, surpassing existing evaluation frameworks and judge models.
- xFinder demonstrates excellent performance in generalization tests, showing its applicability across different tasks.
Significance
The introduction of xFinder addresses inefficiencies in key answer extraction in LLM evaluation, enhancing fairness and reliability. It has significant implications for academia and industry, especially in scenarios requiring high-precision evaluation.
Technical Contribution
xFinder provides a more accurate and efficient evaluation method than RegEx and existing judge models by constructing the KAF dataset and optimizing the answer extraction module. Its innovation lies in improving generalization and accuracy.
Novelty
xFinder is the first to use LLMs for automated evaluation, significantly improving accuracy and efficiency. Compared to traditional methods, xFinder offers unique innovations in answer extraction and matching.
Limitations
- xFinder may still err in handling non-standard answer formats, particularly in complex mathematical problems.
- The model's training and evaluation rely on specific datasets, which may limit its application in other domains.
Future Work
Future work will focus on expanding xFinder's application scope, exploring its applicability in more fields, and further optimizing the model to enhance evaluation accuracy and efficiency.
AI Executive Summary
The rapid advancement of large language models (LLMs) presents challenges in evaluating their performance. Existing methods, such as using regular expressions (RegEx) for answer extraction, often fail due to non-standard formats. xFinder significantly improves evaluation accuracy and efficiency by optimizing the key answer extraction module.
The core of xFinder is the construction of a specialized dataset, KAF, and training with a 500M parameter model. Experimental results show that xFinder achieves 93.42% extraction accuracy on the KAF dataset, with a final judgment accuracy of 97.61%, far surpassing existing methods.
xFinder's innovation lies in its efficient answer extraction and matching capabilities, addressing key issues in LLM evaluation. In the future, xFinder is expected to be applied in more fields, further promoting fair and reliable evaluation of LLMs.
Deep Analysis
Background
As large language models advance, the need for evaluating their performance increases. Traditional methods like regular expressions (RegEx) often fail in extracting answers, leading to unreliable evaluation results. Recently, researchers have tried using fine-tuned LLMs as judge models, but these methods face challenges in generalization and fairness.
Core Problem
Existing evaluation frameworks often fail in extracting key answers, especially when dealing with non-standard formats. This leads to unreliable evaluation results, affecting the fair assessment of LLMs.
Innovation
xFinder significantly improves evaluation accuracy by optimizing the key answer extraction module. Its innovation lies in using LLMs for answer extraction and matching, combined with a self-consistency strategy, addressing the limitations of RegEx methods.
Methodology
- �� Construct the KAF dataset to support model training and evaluation.
- �� Train using a 500M parameter model, optimizing the answer extraction module.
- �� Combine with a self-consistency strategy to enhance extraction and matching accuracy.
Experiments
Experiments used the KAF dataset for evaluation, comparing xFinder with existing evaluation frameworks. Results show that xFinder significantly outperforms RegEx methods in extraction accuracy and judgment accuracy.
Results
xFinder achieves 93.42% extraction accuracy on the KAF dataset, with a final judgment accuracy of 97.61%. Compared to existing methods, xFinder shows more stable performance across different tasks, demonstrating its generalization.
Applications
xFinder can be used in scenarios requiring high-precision evaluation, such as academic research and industrial applications. Its efficient answer extraction and matching capabilities provide significant advantages in evaluating LLM performance.
Limitations & Outlook
xFinder may still err in handling complex mathematical problems and non-standard answer formats. Future work will focus on expanding its application scope and further optimizing the model.
Plain Language Accessible to non-experts
Imagine working in a large library, tasked with checking the correctness of each book's content. Traditional methods are like using a fixed answer book for comparison, but if the book's format differs, you make errors. xFinder is like a smart assistant that understands different book formats and accurately finds the answers.
ELI14 Explained like you're 14
Imagine playing a game where you have to answer questions. Before, you could only use a fixed answer sheet to check right or wrong, but sometimes the answer sheet didn't work. xFinder is like a super-smart game assistant that helps you find the right answer, even if the question format is weird!
Glossary
Large Language Model (LLM)
A large-scale neural network model capable of generating and understanding human language.
Used for generating and evaluating natural language text.
Regular Expression (RegEx)
A tool used for matching string patterns.
Used in traditional evaluation frameworks for answer extraction.
KAF Dataset
A dataset created specifically for training and evaluating xFinder.
Supports the training of the xFinder model.
Self-consistency Strategy
A method to improve model accuracy by generating and comparing multiple results.
Used to enhance xFinder's answer extraction accuracy.
Generalization Capability
The ability of a model to perform well on unseen data.
xFinder's performance across different tasks demonstrates its generalization capability.
Open Questions Unanswered questions from this research
- 1 How to improve xFinder's accuracy in handling complex mathematical problems?
- 2 What is xFinder's applicability in other domains?
- 3 How to further optimize xFinder's model structure to improve efficiency?
Applications
Immediate Applications
Academic Research
Researchers can use xFinder to evaluate LLM performance, improving research accuracy and efficiency.
Long-term Vision
Industrial Applications
xFinder can be used for automated evaluation in the industry, reducing human intervention and improving evaluation reliability.
Abstract
The continuous advancement of large language models (LLMs) has brought increasing attention to the critical issue of developing fair and reliable methods for evaluating their performance. Particularly, the emergence of cheating phenomena, such as test set leakage and prompt format overfitting, poses significant challenges to the reliable evaluation of LLMs. As evaluation frameworks commonly use Regular Expression (RegEx) for answer extraction, models may adjust their responses to fit formats easily handled by RegEx. Nevertheless, the key answer extraction module based on RegEx frequently suffers from extraction errors. Furthermore, recent studies proposing fine-tuned LLMs as judge models for automated evaluation face challenges in terms of generalization ability and fairness. This paper comprehensively analyzes the entire LLM evaluation chain and demonstrates that optimizing the key answer extraction module improves extraction accuracy and enhances evaluation reliability. Our findings suggest that improving the key answer extraction module can lead to higher judgment accuracy and improved evaluation efficiency compared to the judge models. To address these issues, we propose xFinder, a novel evaluator for answer extraction and matching in LLM evaluation. As part of this process, we create a specialized dataset, the \textbf{K}ey \textbf{A}nswer \textbf{F}inder (KAF) dataset, to ensure effective model training and evaluation. Generalization tests and real-world evaluations show that the smallest xFinder model, with only 500 million parameters, achieves an average extraction accuracy of 93.42\%. In contrast, RegEx accuracy in the best evaluation framework is 74.38\%. The final judgment accuracy of xFinder reaches 97.61\%, outperforming existing evaluation frameworks and judge models.