The 99% Success Paradox: When Near-Perfect Retrieval Equals Random Selection
For most of the history of information retrieval (IR), search results were designed for human consumers who could scan, filter, and discard irrelevant information on their own. This shaped retrieval systems to optimize for finding and ranking more relevant documents, but not keeping results clean and minimal, as the human was the final filter. However, LLMs have changed that by lacking this filtering ability. To address this, we introduce Bits-over-Random (BoR), a chance-corrected measure of retrieval selectivity that reveals when high success rates mask random-level performance. We measure selectivity as $BoR = \log_{2}\left(\frac{\mathrm{P}_{obs}}{\mathrm{P}_{rand}}\right)$, where $\mat...
| Authors | Vyzantinos Repantis, Harshvardhan Singh, Tony Joseph et al. |
|---|---|
| Primary category | cs.IR |
| Published | 14 May 2026 |
| Last updated at source | 14 May 2026 |
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