This model teaches LLMs to reason across tables like a database expert
QueryTableSummarizer++ introduces an end-to-end framework that leverages LLMs to generate summaries from multiple tables based on specific queries, eliminating complex preprocessing steps.
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https://arxiv.org/abs/2412.08970
Original Problem 🤔:
Existing methods for query-focused table summarization rely heavily on preprocessing steps and struggle with multi-table reasoning. They often fail to capture relationships between tables and lack scalability across different domains.
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Solution in this Paper 🛠️:
→ The model uses a generative LLM backbone that directly processes queries and table data without intermediate steps
→ Table-aware pre-training enhances understanding through row-column masking and relationship prediction tasks
→ Query-aligned fine-tuning incorporates contrastive learning to distinguish relevant content
→ Reinforcement learning optimizes summary quality using rewards for relevance, coherence, and brevity
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Key Insights 💡:
→ Direct generation from structured data eliminates information loss from preprocessing
→ Table-aware pre-training significantly improves multi-table reasoning
→ Reinforcement learning with feedback ensures high-quality summaries
→ The model scales effectively with increasing table complexity
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Results 📊:
→ BLEU-4: 51.2%, outperforming baselines by 10%
→ ROUGE-L: 49.8%, showing superior content selection
→ Human evaluation scores: 4.5/5 for relevance, 4.4/5 for coherence
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