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.
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