Confidence scores fail in reasoning tasks for document retrieval
The Magnitude Mirage: Rethinking Confidence for Reasoning-Intensive Retrieval
Information Retrieval
Summary
Sometimes computer systems try to find documents by seeing how similar they are to a question, assuming higher similarity means more confidence in the answer. This approach breaks down for questions that need reasoning, like logic or time-based problems, because the system gives high scores to related but incorrect answers. The authors show this problem happens in many systems and datasets, calling it the Magnitude Mirage. They find better ways to guess confidence by looking at differences between scores, which doesn’t require extra computing and improves decision-making in these tough tasks.
What this means in practice
- •For search engine developers: Improve document retrieval systems to better reject incorrect results on reasoning-heavy queries using score distribution techniques without extra computation.
- •For chatbot engineers: Enhance retrieval-augmented chatbots’ confidence estimates to avoid misleading answers for logic or time-dependent user questions.
Authors
Jamie Holdcroft, Abdelrahman Abdallah, Adam Jatowt
Abstract
Many production RAG systems implement retrieval abstention by thresholding raw similarity scores, implicitly treating score magnitude as a confidence signal. We demonstrate that this practice degrades systematically as queries require reasoning beyond semantic matching. Across 11 retrieval architectures and 28 datasets, neural retrievers consistently assign high similarity scores to semantically related but constraint-violating documents, causing magnitude-based thresholds to collapse toward near-random abstention performance on logical and temporal reasoning tasks---a failure we term the Magnitude Mirage. To address this without computationally expensive alternatives, we conduct a large-scale empirical study of six zero-cost Query Performance Prediction (QPP) metrics across three cognitive tiers: semantic matching (BEIR), logical reasoning (BRIGHT), and temporal reasoning (TEMPO). Our central finding is that the key improvement comes from abandoning magnitude in favor of score-distribution signals: the gain from this shift exceeds the differences among distributional alternatives by a factor of 5-10$\times$. In particular, Score Gap ($s_1 - s_k$) and a practical adaptation of Score Magnitude and Variance (LSMV) improve abstention AUROC by up to 0.16 in settings where magnitude-based confidence provides little discriminative power. These methods require no additional inference, retraining, or latency, making them a practical zero-cost replacement for magnitude thresholding in deployed RAG systems.