arXiv:2606.28916v1 Announce Type: cross Abstract: We introduce GRAB, a constructor-encoder-bridge pipeline for table question answering. Our method lifts relational data into an heterogeneous graph, encodes it via message passing, and transfers the signals to an LLM through a small set of query-conditioned latent tokens. This provides the LLM with a compact, task-relevant structural representation together with the flattened text. Crucially, the LLM remains strictly frozen to preserve its general reasoning capabilities; we train only the lightweight graph encoder and latent bridge (91M paramet
The proliferation of complex, multi-table datasets and the limitations of current LLM approaches motivate the development of more efficient and accurate table question answering methods.
This development offers a significant advancement in allowing LLMs to interpret and respond to queries over structured data, expanding their utility in enterprise and analytical contexts.
The ability for LLMs to effectively leverage relational data with minimal parameter burden shifts the paradigm for data interaction, enhancing automation and insight generation.
- · Enterprise AI providers
- · Data analytics platforms
- · Businesses with complex datasets
- · Traditional data querying tools
- · Knowledge management systems reliant on manual structuring
Improved accuracy and efficiency in AI-driven data analysis and reporting.
Accelerated adoption of AI for complex data querying, reducing human effort in data interpretation.
Emergence of new AI-powered business intelligence tools that dynamically generate insights from multi-source enterprise data.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI