🚨New LLM benchmark🚨 We're releasing BiomedSQL🔬 for tabular reasoning over large-scale biomedical databases. This includes questions based on implicit scientific conventions—like statistical thresholds, effect direction, and drug approval status. 📄 Preprint: arxiv.org/pdf/2505.20321 📊 Dataset: huggingface.co/datasets/NIH-… Lead by Matt Koretsky at @DataTecnica

Long-form inputs (e.g., needle-in-haystack setups) are the crucial aspect of high-impact LLM applications. While previous studies have flagged issues like positional bias and distracting documents, they've missed a crucial element: the size of the gold/relevant context. In our latest study, we look into how the size of these gold contexts impacts LLM performance in needle-in-a-haystack scenarios. The verdict? **Smaller gold contexts severely amplify positional bias.** Why should you care? If you're developing LLMs to sift through large number of documents of varying sizes, beware: a smaller gold document among larger distractions can throw your pipeline off course. Basically, practitioners needs to keep an eye not only on the position of the likely gold document but also on its size relative to others. 📄Read the preprint: arxiv.org/abs/2505.18148 Work lead by Owen Bianchi and other collaborators at @DataTecnica