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New framework traces LLM hallucination patterns in Text-to-SQL generation

By PulseAugur Editorial · [1 sources] ·

A new evaluation framework has been developed to analyze hallucination patterns in Text-to-SQL generation by large language models. This framework decomposes queries into simpler sub-queries, allowing for the identification of recurrent hallucinations (errors appearing at multiple stages) and emergent hallucinations (errors appearing only in the final query). The study found that several hallucination categories exhibit significant recurrence, while others, like clause abuse, tend to emerge later in the generation process. The presence of conversational history generally increases recurrence and decreases emergence, highlighting the importance of trajectory-level analysis beyond just the final output. AI

IMPACT Provides a novel method for evaluating LLM accuracy in complex data querying tasks, potentially improving model reliability.

RANK_REASON This is a research paper detailing a new evaluation framework for LLM text-to-SQL generation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework traces LLM hallucination patterns in Text-to-SQL generation

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This is a research paper detailing a new evaluation framework for LLM text-to-SQL generation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hayden Moore, Abhinav Verma, Anshuman Dutt ·

    Tracing Recurrent and Emergent Hallucination Patterns in Decomposed Text-to-SQL Generation

    arXiv:2610.27720v1 Announce Type: new Abstract: Large language models can achieve strong Text-to-SQL benchmark performance while still producing schema and query-logic errors. Most evaluations characterize such failures only in the final generated query, making it difficult to de…

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