Latent Bridges for Multi-Table Question Answering
📰 ArXiv cs.AI
Learn how to improve multi-table question answering using Latent Bridges and a frozen LLM, enabling more accurate and efficient querying of relational data
Action Steps
- Build a heterogeneous graph from relational data using a constructor
- Encode the graph via message passing to capture structural relationships
- Transfer signals to an LLM through query-conditioned latent tokens
- Use the LLM to generate answers based on the compact structural representation and flattened text
- Evaluate the performance of the model on multi-table question answering tasks
Who Needs to Know This
Data scientists and AI engineers working on question answering systems can benefit from this approach to improve the accuracy and efficiency of their models, especially when dealing with complex relational data
Key Insight
💡 Latent Bridges enable the transfer of structural information from relational data to an LLM, improving question answering accuracy without requiring LLM fine-tuning
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🤖 Improve multi-table QA with Latent Bridges and frozen LLMs! 📈
Key Takeaways
Learn how to improve multi-table question answering using Latent Bridges and a frozen LLM, enabling more accurate and efficient querying of relational data
Full Article
Title: Latent Bridges for Multi-Table Question Answering
Abstract:
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 genera
Abstract:
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 genera
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