How Can We Evaluate Instruction Tuned LLM?

Evaluating fine-tuned LLMs involves a comprehensive approach combining quantitative metrics and qualitative human evaluation. This ensures that the models not only perform well on standard benchmarks but also meet human expectations in real-world applications.

When evaluating fine-tuned large language models (LLMs), several metrics can be used to assess their performance across different tasks. The choice of metrics depends on the specific task the model is fine-tuned for. Here are some common evaluation metrics for various NLP tasks. 

In this article, we will explore the most common NLP tasks that LLM can do and the evaluation metrics used for each one. Our goal is to provide you with a brief introduction to these topics, enabling you to conduct further research and gain a deeper understanding in a straightforward manner.

Table of Contents:

1. Introduction

2. Text Classification

3. Named Entity Recognition (NER)

4. Text Generation

5. Question Answering

6. Sentiment Analysis

7. Summarization

8. General Considerations

9. Choosing Metrics

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1. Text Classification

Text Classification involves categorizing text into predefined labels or classes. This task is fundamental in many applications such as spam detection, sentiment analysis, topic labeling, and document categorization. Models are trained to assign a category to each text input based on its content. We can use similar evaluation metrics for classification tasks: 

• Accuracy: The proportion of correctly classified instances among the total instances.

• Precision: The proportion of true positive instances among the predicted positive instances.

• Recall (Sensitivity): The proportion of true positive instances among the actual positive instances.

• F1 Score: The harmonic mean of precision and recall, is useful when the class distribution is imbalanced.

• ROC-AUC: The area under the receiver operating characteristic curve, measuring the ability of the classifier to distinguish between classes.

2. Named Entity Recognition (NER)

Named Entity Recognition (NER) is a task that involves identifying and classifying named entities within text into predefined categories such as person names, organizations, locations, dates, and more. This task is crucial for information extraction, enabling structured data generation from unstructured text. We can use similar evaluation metrics as in the text classification task:

1. Precision: The proportion of correctly identified named entities among all identified named entities.

2. Recall: The proportion of correctly identified named entities among all actual named entities.

3. F1 Score: The harmonic mean of precision and recall for named entity recognition.

3. Text Generation

Text Generation involves producing coherent and contextually relevant text based on a given input or prompt. This task includes applications like language translation, automated content creation, and dialogue systems. The goal is to generate human-like text that is fluent and contextually appropriate. Here are the evaluation metrics that we can use to evaluate the results of this task:

• Perplexity: A measure of how well a probability model predicts a sample, lower perplexity indicates better performance.

• BLEU (Bilingual Evaluation Understudy Score): Measures the overlap between the generated text and reference texts, often used in machine translation.

• ROUGE (Recall-Oriented Understudy for Gisting Evaluation): Measures the overlap of n-grams between the generated text and reference texts, commonly used for summarization tasks.

• METEOR (Metric for Evaluation of Translation with Explicit ORdering): Considers precision, recall, stemming, and synonymy to evaluate the quality of the generated text.

4. Question Answering

Question Answering (QA) systems aim to provide accurate and concise answers to questions posed in natural language. QA can be open-domain, where the system answers questions on any topic, or closed-domain, focusing on specific subject areas. This task is critical for virtual assistants and search engines. We can use the Exact Match and F1-Score to evaluate the results:

1. Exact Match (EM): The percentage of predictions that match any one of the ground truth answers exactly.

2. F1 Score: Considers the overlap between the predicted and ground truth answers, treating the prediction as a bag of words.

5. Sentiment Analysis

Sentiment Analysis involves determining the sentiment expressed in a piece of text and categorizing it as positive, negative, or neutral. This task is widely used in opinion mining, customer feedback analysis, and social media monitoring to gauge public sentiment towards products, services, or events. The evaluation metrics used for this task are similar to classification tasks:

1. Accuracy: The proportion of correctly predicted sentiments.

2. Precision, Recall, and F1 Score: Similar to text classification, these metrics evaluate the performance of each sentiment class.

6. Summarization

Summarization aims to condense a longer text into a shorter version, capturing the main ideas and essential information. There are two main types: extractive summarization, which selects key sentences from the original text, and abstractive summarization, which generates new sentences to represent the summary. This task is useful for creating concise overviews of large documents or articles. There are two evaluation metrics that are commonly used it in this task:

1. ROUGE: Measures the overlap of n-grams between the generated summary and reference summaries.

2. BLEU: Measures the quality of the generated summary against reference summaries.

7. General Considerations

When evaluating the performance of fine-tuned large language models (LLMs), it is crucial to consider both quantitative and qualitative measures to comprehensively understand the model’s capabilities and limitations. 

This is particularly important because LLMs are applied to a wide range of natural language processing (NLP) tasks that often require nuanced understanding and generation of human language. Here, we delve into two key aspects of this evaluation: Human Evaluation and the Confusion Matrix.

• Human Evaluation: Often used in conjunction with automated metrics, human evaluation can provide insights into the quality, fluency, coherence, and relevance of the model outputs.

• Confusion Matrix: Useful for understanding the performance of classification models by displaying the true positives, true negatives, false positives, and false negatives.

8. Choosing Metrics

The choice of metrics should align with the specific goals and requirements of the task. For instance:

• In a text generation task, BLEU and ROUGE might be more appropriate.

• For a classification task, accuracy, precision, recall, and F1 score are commonly used.

• For question answering, exact match and F1 score are prevalent.

Combining multiple metrics often provides a more comprehensive evaluation of the model’s performance. Additionally, incorporating human judgment is crucial for tasks requiring nuanced understanding or creative generation to capture aspects that automated metrics might miss.