Sufficient Context Analysis for Reliable RAG Systems
This blog post summarizes a recent study that introduces a new perspective on Retrieval Augmented Generation (RAG) systems, focusing on the concept of "sufficient context" and its impact on model performance. The study addresses the critical question: Are RAG failures due to the LLM's inability to use the provided context, or is the context itself insufficient?
Problem Definition
- RAG systems often fail to provide accurate answers, but the underlying reasons for these failures are not always clear.
- It's crucial to distinguish between cases where the context contains enough information to answer the query (sufficient context) and cases where it does not.
- Current open-book question answering benchmarks may contain a significant fraction of instances with insufficient context.
Proposed Solution
- The authors introduce a formal definition of "sufficient context," defining it as context containing enough information to construct a plausible answer to a given question.
- They develop an LLM-based autorater, leveraging Gemini 1.5 Pro, to classify instances as having sufficient or insufficient context, achieving 93% accuracy. A computationally efficient alternative is FLAMe.
- A selective generation framework is implemented, which uses the sufficient context labels and model confidence scores to improve accuracy by guiding abstention.
Results
- Model performance varies significantly based on context sufficiency:
- Larger models like Gemini 1.5 Pro, GPT 4o, and Claude 3.5 perform well when the context is sufficient but tend to hallucinate when it is not.
- Smaller models such as Mistral 3 and Gemma 2 may hallucinate or abstain even with sufficient context.
- The selective generation method improves the fraction of correct answers by 2-10% for Gemini, GPT, and Gemma.
- Analysis of standard datasets (FreshQA, Musique-Ans, and HotpotQA) reveals that they often contain a notable percentage of instances with insufficient context.
Importance
- The study highlights that improving retrieval alone is not enough to solve open-book question answering tasks.
- Selective generation, using sufficient context information, can effectively reduce hallucinations in RAG systems.
- The research deepens the understanding of how models generate responses with retrieval, revealing nuances in RAG systems.
Technical Details
- Sufficient Context Definition: An instance q' = (Q, C) has sufficient context if and only if there exists an answer A' such that A' is a plausible answer to the question Q given the information in C.
- Selective Generation: Combines sufficient context autorater outputs with model self-rated confidence scores to tune a selective accuracy-coverage trade-off.
- Evaluation Metrics: Accuracy, Precision, Recall, and F1 Score are used, along with LLMEval for assessing answer sameness.
- Baselines: TRUE-NLI (fine-tuned entailment model) and Contains GT (checks if a GT answer appears in the context) are used as baselines.