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The Hong Kong University of Science and Technology
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ErrorLLM tackles the challenge of refining LLM-generated SQL by explicitly modeling and detecting implicit semantic errors, leading to substantial improvements in text-to-SQL performance.
Current memory systems like RAG and long-context LLMs stumble in AMemGym's interactive long-horizon conversations, revealing critical performance gaps in maintaining consistent user state.
NGDB-Zoo unlocks up to 6.8x faster training for Neural Graph Databases by decoupling logical operators and integrating semantic priors from pre-trained text encoders, all while maintaining high GPU utilization.
