LakeQuest: A Three-Domain Benchmark for Grounded Question Answering across Data Lakes

2025-12-01 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

LakeQuest is a new human-validated benchmark comprising 9,846 question-answering pairs designed to evaluate end-to-end retrieve-and-synthesize pipelines over realistic, heterogeneous data lakes. It addresses the gap where existing benchmarks fail to assess performance on weakly structured collections of tables, passages, and linked metadata. The benchmark spans three diverse domains: AI/ML metadata, retail banking, and multimodal biomedical drug information. Each question is paired with exact, modality-aware evidence pointers, allowing for the isolation of source discovery from cross-modal synthesis. Baseline evaluations, including RAG and agentic tool-use methods, reveal that high retrieval quality does not consistently translate into correct reasoning, with systems struggling across relation chaining, policy grounding, and joint tabular QA.

Key takeaway

For AI Scientists and Machine Learning Engineers developing enterprise QA systems, you should prioritize robust cross-source composition and faithful attribution mechanisms over mere retrieval quality. Your designs must explicitly address the synthesis gap, especially when integrating unstructured policies with structured ledgers or combining multimodal evidence. Be mindful that agentic approaches, while powerful, significantly increase token consumption and latency, impacting real-time operational environments.

Key insights

LakeQuest benchmarks end-to-end QA over heterogeneous data lakes, revealing retrieval success doesn't guarantee reasoning accuracy.

Principles

• Real-world QA needs end-to-end evaluation.
• Isolate discovery and synthesis failure modes.
• High retrieval quality does not guarantee reasoning.

Method

A five-stage pipeline (Lake Instantiation, Candidate Synthesis via GPT-5, Feb. 1, 2026, Structural Filtering, Semantic Filtering, Human Validation) generates QA pairs with explicit evidence traces.

In practice

• Evaluate QA systems on Accuracy, Faithfulness, Retrieval Quality.
• Implement modality-aware provenance for auditability.
• Benchmark agentic methods for cross-modal composition.

Topics

• Data Lake QA
• Grounded Question Answering
• Retrieval-Augmented Generation
• Multimodal Data Integration
• AI/ML Metadata
• Agentic AI Systems

Code references

• michael0402/LakeQuest-starter-kit

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.