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New LakeQA benchmark challenges LLMs with massive data search and reasoning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced LakeQA, a new benchmark designed to test the capabilities of large language models in searching and reasoning over massive data lakes. The benchmark utilizes approximately 9.5 TB of diverse data, including Wikipedia and government datasets, requiring multi-hop reasoning and evidence composition across multiple sources. Initial experiments show that even advanced models like GPT-5.2 struggle with the task, achieving an exact-match score of only 18.37%, highlighting the challenge LakeQA presents for developing effective LLM agents. AI

IMPACT Establishes a new, challenging benchmark for evaluating LLM agents' ability to search and reason over large, unstructured datasets.

RANK_REASON The cluster contains a research paper introducing a new benchmark for evaluating LLMs.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Haonan Wang, Jiaxiang Liu, Yurong Liu, Austin Senna Wijaya, Tianle Zhou, Eden Wu, Yijia Chen, Wanting You, Reya Vir, Daniela Pinto, Grace Fan, Yusen Zhang, Juliana Freire, Eugene Wu ·

    LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

    arXiv:2606.10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved. In contrast, real-world questions are often not paired wi…

  2. arXiv cs.CL TIER_1 English(EN) · Eugene Wu ·

    LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake

    Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved. In contrast, real-world questions are often not paired with accurate evidence documents. The useful evidenc…

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