BAGEL is a Bayesian active learning framework that uses Gaussian Processes to propagate LLM relevance signals across embedding space and guide global exploration, outperforming standard LLM reranking under identical budgets on four retrieval benchmarks.
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Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.
Bits-over-Random shows that retrieval systems reporting over 99% success on datasets like 20 Newsgroups often match random performance once chance is accounted for via hypergeometric baseline.
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Bayesian Active Learning with Gaussian Processes Guided by LLM Relevance Scoring for Dense Passage Retrieval
BAGEL is a Bayesian active learning framework that uses Gaussian Processes to propagate LLM relevance signals across embedding space and guide global exploration, outperforming standard LLM reranking under identical budgets on four retrieval benchmarks.
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Instructions Shape Production of Language, not Processing
Instructions trigger a production-centered mechanism in language models, with task-specific information stable in input tokens but varying strongly in output tokens and correlating with behavior.
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The 99% Success Paradox: When Near-Perfect Retrieval Equals Random Selection
Bits-over-Random shows that retrieval systems reporting over 99% success on datasets like 20 Newsgroups often match random performance once chance is accounted for via hypergeometric baseline.