jiaruz2/MultiTableQA_WTQ
๐ Paper | ๐จ๐ปโ๐ป Code ๐ Introduction Retrieval-Augmented Generation (RAG) has become a key paradigm to enhance Large Language Models (LLMs) with external knowledge. While most RAG systems focus on text corpora, real-world information is often stored in tables across web pages, Wikipedia, and relational databases. Existing methods struggle to retrieve and reason across multiple heterogeneous tables. For MultiTableQA, we release a comprehensive benchmark, including fiveโฆ See the full description on the dataset page: https://huggingface.co/datasets/jiaruz2/MultiTableQA_WTQ.
| Author / organization | jiaruz2 |
|---|---|
| License (as declared) | mit |
| Downloads | 170 (as reported by Hugging Face) |
| Likes | 0 |
| Created | 1 October 2025 |
| Last updated at source | 7 October 2025 |
Recent changes
- 16 Oct 2026 Description: ๐ Paper | ๐จ๐ปโ๐ป Code ๐ Introduction Retrieval-Augmented Generation (RAG) has become a key paradigm to enhance Large Language Models (LLMs) with external knowledge. While most RAG systems focus on text corpora, real-world information is โ ๐ Paper | ๐จ๐ปโ๐ป Code ๐ Introduction Retrieval-Augmented Generation (RAG) has become a key paradigm to enhance Large Language Models (LLMs) with external knowledge. While most RAG systems focus on text corpora, real-world information is
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