LakeQuest Paper at COLM: A QA Benchmark for Messy, Real-World Data Lakes
hllo_wrld · x · 2026-08-01
Researchers introduced LakeQuest, a benchmark designed to evaluate systems' Question Answering (QA) capabilities over realistic and messy data lakes. The paper was accepted at the COLM conference.
Key Highlights:
- Background: While modern QA systems excel on clean, schema-aligned corpora, real-world enterprise and scientific data is heterogeneous and weakly structured, consisting of tables, passages, and broken metadata.
- Benchmark Design: LakeQuest features 9,846 human-validated QA pairs across three diverse domains (AI/ML metadata, retail banking, and multimodal biomedical drug info). Every question is paired with exact, modality-aware evidence pointers.
- Findings: Baseline evaluations of standard RAG and agentic tool-use methods reveal that high-quality retrieval does not guarantee correct reasoning. Systems consistently struggle with critical failure modes like relation chaining in metadata graphs, policy grounding in bank ledgers, and joint tabular QA in biomedical contexts.
Related event: LakeQuest: A New Benchmark for Messy Data Lake QA(2 posts)→
More from Research
- Humanoid Robot Dodges 19/20 Thrown Balls Using Onboard Sensors — ChongZzZhang · 2026-08-01
- TMLR Adopts Fractional Authorship, Weighing Credit by 1/k per Author — thegautamkamath · 2026-08-01
- ACE-Data-0: A Large-Scale Multimodal Dataset for Embodied AI — liuziwei7 · 2026-08-01
- Waterloo's R2L Lab to Recruit PhDs, Focusing on Agents and Reasoning Research — hllo_wrld · 2026-08-01
- AgentIR: Deep Research Agents That Leverage Reasoning Context for Retrieval — hllo_wrld · 2026-08-01
- Looped Model Architecture: 8B Params Outperform 32B in Reasoning — SonglinYang4 · 2026-08-01