RAG - Hallucination Detection
Hallucination Hallucination means making an assumption or making up something when the LLM does not know the answer. Hallucination in RAG Example: Suppose we have a PDF file that contains information about Python, but it does not have any details about Decorators . Here, the document is split into chunks, and the chunks are stored in the vector database. Suppose we ask the query: "What is a…
Hallucination detection is crucial in Retrieval-Augmented Generation (RAG) systems. Hallucination occurs when the Language Model (LLM) generates incorrect information or assumptions due to lack of data in the queried database. For example, if a PDF document on Python lacks details about Decorators, asking "What is a decorator?" might result in an incorrect response from the LLM, even though it's not based on the database information. This is known as hallucination in RAG.
To measure hallucination knowledge and groundedness, the LLM's response must be supported by the context. If the response lacks context support, it is considered hallucinated. Several methods can be used to detect hallucinations:
1. Embedding: By comparing the embedding of the context with the LLM's response, their closeness can indicate the likelihood of hallucination.
2. LettuceDetect: A BERT-based model that checks the response word by word to identify potential hallucinations at the character level.
3. LLM as a Judge: The response is given to another LLM, which then attempts to detect hallucinations. However, this method has drawbacks, such as the possibility of the second LLM hallucinating and potential costs if a local model is required.
4. RAGAS Framework: An evaluation framework that provides metrics to evaluate RAG systems and identify potential hallucinations. Some important metrics include:
- Faithfulness: Checks if results are supported by the provided context.
- Answer Relevancy: Assesses how relevant the answer is to the query.
- Context Precision: Evaluates the relevance of the retrieved documents.
- Context Recall: Determines if necessary documents have been retrieved for the query.
- Answer Correctness: Compares the response against the ground truth.
- Answer Similarity: Evaluates the semantic similarity between the expected answer and the generated answer.
RAGAS can be integrated into a CI/CD pipeline, where unit test cases compare the actual answer with the expected answer using these metrics. Additionally, DeepEval can also be used to evaluate RAG systems and detect hallucinations.
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