Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQL

• URL: http://arxiv.org/abs/2511.10674v1
• Date: Mon, 10 Nov 2025 05:29:10 GMT
• Title: Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQL
• Authors: Thomas Cook, Kelly Patel, Sivapriya Vellaichamy, Saba Rahimi, Zhen Zeng, Sumitra Ganesh,
• Abstract summary: We introduce a framework for continual learning from human feedback in text-to-aware schemas and distilled schemas.<n>We show that memory-augmented agents, particularly the Procedural Agent, achieve significant accuracy gains and error reduction by leveraging human-in-the-loop feedback.
• Score: 9.964158093998277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) can generate SQL queries from natural language questions but struggle with database-specific schemas and tacit domain knowledge. We introduce a framework for continual learning from human feedback in text-to-SQL, where a learning agent receives natural language feedback to refine queries and distills the revealed knowledge for reuse on future tasks. This distilled knowledge is stored in a structured memory, enabling the agent to improve execution accuracy over time. We design and evaluate multiple variations of a learning agent architecture that vary in how they capture and retrieve past experiences. Experiments on the BIRD benchmark Dev set show that memory-augmented agents, particularly the Procedural Agent, achieve significant accuracy gains and error reduction by leveraging human-in-the-loop feedback. Our results highlight the importance of transforming tacit human expertise into reusable knowledge, paving the way for more adaptive, domain-aware text-to-SQL systems that continually learn from a human-in-the-loop.

Related papers

• Prompt Tuning for Natural Language to SQL with Embedding Fine-Tuning and RAG [1.988259704465628]
  We propose a novel framework integrating an error correction mechanism that diagnoses error types and identifies their causes.<n>We demonstrate that our framework achieves a significant 12 percent accuracy improvement over existing baselines.
  arXiv  Detail & Related papers  (2025-11-11T13:41:13Z)
• ORANGE: An Online Reflection ANd GEneration framework with Domain Knowledge for Text-to-SQL [8.241433772695018]
  Large Language Models (LLMs) have demonstrated remarkable progress in translating natural language tosql.<n>A significant semantic gap persists between their general knowledge and domain-specific semantics of databases.<n>We introduce Orange, an online self-evolutionary framework that constructs database-specific knowledge bases by parsing queries from translation logs.
  arXiv  Detail & Related papers  (2025-11-02T15:57:18Z)
• Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization [56.674356045200696]
  We propose a novel method to train AI agents to incorporate knowledge and skills for multiple tasks without the need for cumbersome note systems or prior high-quality demonstration data.<n>Our approach employs an iterative process where the agent collects new experiences, receives corrective feedback from humans in the form of hints, and integrates this feedback into its weights.<n>We demonstrate the efficacy of our approach by implementing it in a Llama-3-based agent that, after only a few rounds of feedback, outperforms advanced models GPT-4o and DeepSeek-V3 in tasksets.
  arXiv  Detail & Related papers  (2025-02-03T17:45:46Z)
• In-Memory Learning: A Declarative Learning Framework for Large Language Models [56.62616975119192]
  We propose a novel learning framework that allows agents to align with their environment without relying on human-labeled data. This entire process transpires within the memory components and is implemented through natural language. We demonstrate the effectiveness of our framework and provide insights into this problem.
  arXiv  Detail & Related papers  (2024-03-05T08:25:11Z)
• Generative Multi-Modal Knowledge Retrieval with Large Language Models [75.70313858231833]
  We propose an innovative end-to-end generative framework for multi-modal knowledge retrieval. Our framework takes advantage of the fact that large language models (LLMs) can effectively serve as virtual knowledge bases. We demonstrate significant improvements ranging from 3.0% to 14.6% across all evaluation metrics when compared to strong baselines.
  arXiv  Detail & Related papers  (2024-01-16T08:44:29Z)
• Towards A Unified Agent with Foundation Models [18.558328028366816]
  We investigate how to embed and leverage such abilities in Reinforcement Learning (RL) agents. We design a framework that uses language as the core reasoning tool, exploring how this enables an agent to tackle a series of fundamental RL challenges. We demonstrate substantial performance improvements over baselines in exploration efficiency and ability to reuse data from offline datasets.
  arXiv  Detail & Related papers  (2023-07-18T22:37:30Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)
• Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge [54.85168428642474]
  We build a new Chinese benchmark Know consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing.
  arXiv  Detail & Related papers  (2023-01-03T12:37:47Z)
• Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language [121.56329458876655]
  We introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld. We propose the "Asking for Knowledge" (AFK) agent, which learns to generate language commands to query for meaningful knowledge.
  arXiv  Detail & Related papers  (2022-05-12T14:20:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.