Understanding Evaluation of Retrieval Augmented Generation in LLMs

🔍 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.

📚 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.

🔍 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.

📊 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.

🔍 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.

🔍 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.

🔍 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.