FollowTable is the first large-scale benchmark for instruction-following table retrieval, paired with an Instruction Responsiveness Score, showing that existing models fail to adapt to fine-grained constraints beyond topical similarity.
In: Proceedings of the 44th International ACM SIGIR Confer- ence on Research and Development in Information Retrieval
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Cross-encoder reranker performance scales predictably via power laws with model size and training exposure, allowing accurate forecasts for 400M and 1B models and data-heavy compute allocation.
PIPER retrieves and ranks tabular datasets by profiling their content and using LLM-generated queries for dense vector search, outperforming metadata baselines and TableQA methods in low-metadata settings.
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FollowTable: A Benchmark for Instruction-Following Table Retrieval
FollowTable is the first large-scale benchmark for instruction-following table retrieval, paired with an Instruction Responsiveness Score, showing that existing models fail to adapt to fine-grained constraints beyond topical similarity.
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Scaling Laws for Cross-Encoder Reranking
Cross-encoder reranker performance scales predictably via power laws with model size and training exposure, allowing accurate forecasts for 400M and 1B models and data-heavy compute allocation.
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PIPER: Content-Based Table Search via profiling and LLM-Generated Pseudoqueries
PIPER retrieves and ranks tabular datasets by profiling their content and using LLM-generated queries for dense vector search, outperforming metadata baselines and TableQA methods in low-metadata settings.