Understanding Evaluation of Retrieval Augmented Generation in LLMs

This video explores retrieval augmented generation using two models: retriever and generator. It discusses evaluating context precision and recall for retriever LLMs.

00:00:00 Evaluate LLMs - RAG: Enhancing language models with retrieval augmented generation to improve question answering in limited context.

🔍 Retrieval augmented generation is a method used to improve the accuracy of language models like GPT and Pal.

The relevance of the training data to the question determines the accuracy of the language model's answer.

💡 To overcome token limitations in the context, all documents are utilized and additional information is provided when asking questions.

00:01:22 Using an LLM model, we divide a book into small chunks and create embedding vectors for each page. These vectors are stored in a database and used to retrieve similar documents when asking a question to the LLM model, overcoming context length limitations.

📚 Using LLM models, we can create embeddings for a book divided into small chunks.

🔎 We use the embedding vectors to retrieve similar documents as context for asking questions to the LLM model.

💡 By overcoming the challenge of context length, we are able to extract accurate answers.

00:02:42 This video explains the concept of retrieval augmented generation using two models: retriever and generator. It also discusses the evaluation process for both models.

🔍 LLMs consist of a retriever and a generator.

Evaluation of LLMs involves assessing both the retriever and the generator.

📊 Performance evaluation is done based on the retrieved context and the generated answer.

00:04:03 This video discusses two metrics, context precision and context recall, for evaluating retriever LLMs. Context precision measures the relevance of the retrieved context to the question. The value ranges between 0 and 1.

📊 Context precision measures how relevant the retrieved context is to the question.

🔎 Context recall measures how good the retrieved context is.

⚖️ The value of context precision ranges between 0 and 1, with higher values indicating better relevance.

00:05:23 This video discusses evaluating the precision and recall of a retriever model and generator model. The context recall measures if the retriever model can extract relevant information. The generator model takes a question and context as inputs and provides an answer.

🔍 Computing context precision and context recall to evaluate the retriever model's ability to extract relevant information.

🔎 Context recall measures the ability to predict important cases correctly based on the ground truth and retrieved context.

The generator takes question and context as input to provide an answer.

00:06:41 This video discusses the evaluation of Language Models by measuring their faithfulness, answer relevancy, precision, recall, and aspect critique.

🔍 Faithfulness is the accuracy of the generated answer and is evaluated by comparing it with the retrieved context.

🔗 Answer relevancy measures how relevant the generated answer is to the given question.

📏 Four metrics are used to evaluate LLMs: faithfulness, answer relevancy, precision, and recall.

00:07:59 In this video, we learn about evaluating LLMs. We explore whether the provided answer is harmful or malicious. We also discuss coherence and the use of the RAG Python library.

🔍 The video discusses the evaluation of LLM models, specifically addressing harmfulness and coherence of answers.

💻 The evaluation takes only the answer as input and checks for harmful or malicious content, providing a boolean output.

🐍 In the next video, a Python library called RAGAS will be used to compute these evaluation metrics.

Summary of a video "Evaluate LLMs - RAG" by Hands-on Data Science & AI on YouTube.

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