TL;DR

1. In traditional deep learning, each model was designed to perform a specific task, and engineers tested each model before deployment.
2. However, when using a large language model, which is intended to fulfill general purposes, it is almost impossible to predict how the model will react or behave. Most of the time, those building an LLM application did not develop the model themselves.
3. Therefore, we need a process to evaluate LLMs and assess how each model performs for our specific tasks to ensure the quality of our application.

Traditional Deep Learning vs Large Language Model

Developing an Application with a Deep Learning Model

Deep Learning Model development involves selecting a model suitable for a specific purpose and preparing the appropriate datasets. It’s crucial to clearly define the model’s objective. In the field of Natural Language Processing (NLP), different tasks often require different models:

• For text classification, models like Recurrent Neural Networks (RNN) or Transformers are commonly used.
• Language translation tasks often employ sequence-to-sequence (seq2seq) models, typically enhanced with attention mechanisms.
• Question-answering systems can utilize models like BERT or RoBERTa.
• Sentiment analysis can benefit from models such as LSTM (Long Short-Term Memory) or GRU (Gated Recurrent Units).
• Speech recognition tasks may use models such as wav2vec or other end-to-end architectures.
• Text generation typically relies on Transformer models, such as GPT (Generative Pre-trained Transformer).

After selecting the appropriate model, the next step is to prepare suitable datasets. Datasets are generally divided into three categories:

• Training Set: The primary data used to train the model.
• Validation Set: Used to evaluate the model’s performance during training and adjust hyperparameters.
• Test Set: Used for the final evaluation of the model’s performance.

Through this process, you can develop and train a Deep Learning Model tailored to your specific objectives.

Developing an Application with Large Language Models

Developing services that utilize Large Language Models (LLMs) differs significantly from traditional AI application development. Here are the key points:

• LLMs are primarily used for text generation.
• Service creation involves crafting prompts to produce desired text outputs.
• Unlike traditional models, developers do not directly train the LLM.
• The training data for models like GPT, Claude, or Mistral is often not transparent.

Regarding datasets for LLM-based services:

• The emphasis is on creating a Test Set.
• This Test Set should be specifically designed for the service being developed.

This approach to service development with LLMs highlights the importance of prompt engineering and rigorous testing, rather than model training, marking a significant shift from traditional deep learning model development.

Understanding LLM Evaluation

What is LLM Evaluation?

LLM Evaluation is the process of measuring how accurately and efficiently a Large Language Model (LLM) performs on given tasks. This involves assessing whether language models like GPT and Claude can provide appropriate responses to user queries, and whether their responses are logical and consistent.

Through evaluation, we can quantitatively and qualitatively measure a model’s performance, identifying areas that need improvement. This is particularly crucial for prompt engineering and when utilizing various commercial models rather than developing them in-house.

Why is LLM Evaluation Important?

Large Language Models (LLMs) have emerged as incredibly powerful tools, revolutionizing various aspects of our lives and enabling capabilities we’ve never seen before. However, with great power comes great responsibility, and it’s crucial to understand both the potential and limitations of these AI marvels.

Here are some key reasons why LLM evaluation is of utmost importance:

• Inconsistency in Outputs: Due to the nature of generative AI, LLMs can produce different answers to the same question each time they’re prompted. This variability necessitates robust evaluation methods to ensure consistent quality and reliability.
• Risk of Hallucination: LLMs can sometimes generate information that seems plausible but is actually incorrect or fabricated. This phenomenon, known as hallucination, underscores the need for thorough evaluation to maintain the accuracy and trustworthiness of the model’s outputs.
• Unpredictable User Interactions: The wide range of potential user inputs and behaviors makes it challenging to anticipate all possible scenarios. Consequently, implementing safety measures becomes crucial to mitigate risks and ensure responsible AI usage.
• Quality Control: Evaluating the style and accuracy of LLM outputs is essential for maintaining high standards and meeting user expectations. This involves assessing factors such as coherence, relevance, and factual correctness.

By prioritizing LLM evaluation, we can harness the full potential of these powerful tools while mitigating risks and ensuring responsible AI development and deployment. As LLMs continue to evolve and integrate into various applications, robust evaluation methods will play a crucial role in building trust, improving performance, and addressing ethical concerns in the AI landscape.

Addressing the Challenges of LLMs

LLMs are incredibly powerful tools that have opened up new possibilities in various fields. However, they also come with limitations and risks that need to be addressed:

• The generative nature of AI means responses can vary each time
• There’s a possibility of hallucination (generating false or nonsensical information)
• Unpredictable user behavior necessitates safety measures
• There’s a need to measure the style and accuracy of outputs

By conducting thorough evaluations, we can harness the potential of LLMs while mitigating their risks

Evaluation Metrics for LLM Applications

When it comes to evaluating Large Language Model (LLM) applications, there are various metrics and approaches available. Let’s explore some of the key evaluation methods and why certain metrics are more suitable for Retrieval Augmented Generation (RAG) applications.

Types of Evaluation Metrics

1. Model-Based Evaluation: This approach utilizes deep learning models specifically designed for assessment purposes.
2. Statistical Metrics: Traditional metrics like BLEU, ROUGE, and perplexity fall into this category.
3. Human Evaluation: This involves direct assessment by human evaluators.
4. RAG Metrics: Specialized metrics designed for Retrieval Augmented Generation systems.

For RAG applications, which are becoming increasingly common in LLM-based services, we’ll focus on RAG-specific metrics.

Why Traditional Statistical Metrics Fall Short for RAG

While metrics like BLEU, ROUGE, and perplexity have been staples in NLP evaluation, they have limitations when it comes to RAG systems:

• BLEU and ROUGE limitations:
  • These metrics primarily measure similarity between generated text and reference text, which is more suitable for translation and summarization tasks.
  • RAG systems require evaluation of information accuracy, relevance, and appropriateness of answers to user queries, which go beyond simple text similarity.
  • The natural conversation flow and information reliability needed in LLM-based RAG systems are not adequately captured by BLEU and ROUGE.
• Perplexity limitations:
  • Perplexity focuses on how well a model has learned text patterns and fluency.
  • In RAG systems, the ability to interact with external knowledge sources and provide accurate information is crucial.
  • Perplexity falls short in evaluating the accuracy and relevance of the information provided.

The Rise of LLM-as-a-Judge

With the advent of powerful language models, there’s a growing trend of using LLMs themselves as judges for evaluation. This approach, known as LLM-as-a-Judge, is gaining popularity in the field. For more information on this approach, you can refer to the paper Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

In conclusion, while traditional metrics have their place in NLP evaluation, RAG systems require more specialized evaluation approaches that can account for the complexities of information retrieval, accuracy, and relevance in the context of user queries and external knowledge sources.

Metrics

When evaluating Large Language Models (LLMs) in Retrieval Augmented Generation (RAG) systems, several key metrics are used to assess performance. These metrics help ensure that the generated responses are accurate, relevant, and faithful to the provided context. Let’s explore some of the most important metrics used in this field according to the paper RAGAS: Automated Evaluation of Retrieval Augmented Generation:

• 1. Faithfulness: Faithfulness measures how well the generated answer aligns with the given context.

This metric is crucial for:

• Preventing hallucinations (fabricated information)

• Ensuring that the provided context can justify the generated answer

• 2. Answer Relevance: This metric evaluates whether the generated answer is directly related to the given question. It helps maintain the focus and accuracy of the response

• 3. Context RelevanceContext Relevance assesses the accuracy and appropriateness of the context used to generate the answer. It ensures that:

  • Only necessary information is used as context
  • The context is neither too long nor too short
  • Take a look at this paper for more information

In addition to these core metrics, the RAGAS package provides several specific metrics for evaluating RAG systems:

• 4. Context Recall: This metric measures whether the system retrieved the necessary context to generate the answer

• 5. Context Precision: Context Precision evaluates the relevance of the retrieved context, focusing on how well the most relevant information is prioritized

• 6. Context Entities Recall: This metric compares the entities in the reference document with those in the retrieved documents, helping to identify how well the system captures important information

• 7. Response Relevancy: Similar to Answer Relevance, this metric assesses how well the generated response relates to the original question

• 8. Noise SensitivityThis metric measures how frequently the system provides inaccurate responses, helping to identify potential weaknesses in the RAG pipeline

The RAGAS package also includes metrics for evaluating multimodal systems, such as Multimodal Faithfulness and Multimodal Relevance

By utilizing these metrics, developers and researchers can comprehensively evaluate the performance of their RAG systems, ensuring that they provide accurate, relevant, and context-appropriate responses to user queries.

In this post, I’ve discussed the importance of evaluating Large Language Models (LLMs) and outlined the key aspects to assess in order to ensure the performance and quality of an LLM application. In the next post, I will delve into the timing and methods for evaluating these applications.