7 articles tagged with #semantic-parsing. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers have introduced Agentics 2.0, a Python framework for building enterprise-grade AI agent workflows using logical transduction algebra. The framework addresses reliability, scalability, and observability challenges in deploying agentic AI systems beyond research prototypes.
SIRIUS-SQL introduces a multi-candidate approach to Text-to-SQL generation that addresses redundancy, execution error classification, and selector limitations through difficulty-smoothing reinforcement learning, targeted repair mechanisms, and hybrid confidence-gated selection. The system achieves 75.88% accuracy on BIRD dev and 91.20% on SPIDER test, surpassing previous state-of-the-art multi-candidate systems.
SkillPager is a novel retrieval framework that optimizes how large language model agents access long procedural documents by selecting minimal, execution-sufficient context from skill documents. The system achieves 78.89% sufficiency while reducing prompt tokens by 47.04% compared to full-document prompting, demonstrating that typed semantic granularity significantly improves efficiency in skill-based LLM agent systems.
SEAL introduces a two-stage semantic parsing framework that combines large language models with agentic learning to improve conversational question answering over knowledge graphs. The system self-evolves through dialog history and execution feedback without retraining, achieving state-of-the-art results on complex multi-hop reasoning and aggregation tasks while reducing computational costs.
Researchers propose a method for large language models to handle ambiguous user requests by generating structured responses that enumerate multiple valid interpretations with corresponding answers, trained via reinforcement learning with dual reward objectives for coverage and precision.
Researchers introduce Legal2LogicICL, an LLM-based framework that improves the conversion of natural-language legal cases into logical formulas through retrieval-augmented few-shot learning. The method addresses data scarcity in legal AI systems and introduces a new annotated dataset (Legal2Proleg) to advance interpretable legal reasoning without requiring model fine-tuning.
Researchers introduced GroundedKG-RAG, a new retrieval-augmented generation system that creates knowledge graphs directly grounded in source documents to improve long-document question answering. The system reduces resource consumption and hallucinations while maintaining accuracy comparable to state-of-the-art models at lower cost.
