mlsanityData to 14 Oct 2026

StocksTalk: A Voice-Enabled Conversational Agent for Structured Query Generation over Web Data

arXiv 2608.18105 ↗First posted 10 Jun 2026cs.CL cs.AI cs.LGcited by 0

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

StocksTalk is a voice-enabled conversational system for transforming spoken financial screening requests into executable and validated structured queries over real-world market data. The system combines streaming speech recognition, retrieval-augmented constraint extraction, schema-grounded LLM-based SQL generation, rule-based validation, and human-in-the-loop verification within an interactive dashboard. Unlike traditional template-driven financial assistants, StocksTalk exposes intermediate reasoning artifacts, including extracted constraints, normalized financial metrics, operator grounding, and generated queries, allowing users to inspect and refine each stage before execution. To evaluate the system, we curate a benchmark of 150 spoken financial prompts spanning multiple investment strategies and input noise conditions. Experimental results show that retrieval grounding, constrained query generation, and interactive verification substantially improve constraint extraction accuracy, SQL executability, logical consistency, and multi-turn stability compared to baseline LLM-based approaches. StocksTalk demonstrates how transparent, voice-driven interfaces can bridge natural language interaction and structured financial analysis, providing an effective framework for conversational stock screening and decision support.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 12 references that are in the map. First posted on arXiv 10 Jun 2026.

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8 of 12 references.

References · 8

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  3. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAGcited by 249
  4. 2025A Survey of Context Engineering for Large Language Modelscited by 126
  5. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
  6. 2025A Survey on Knowledge-Oriented Retrieval-Augmented Generationcited by 36
  7. 2023Enhancing Text-to-SQL Translation for Financial System Designcited by 12
  8. 2025Agentic Workflows for Conversational Human-AI Interaction Designcited by 8

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  1. 2026FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval
  2. 2024An Agent Framework for Real-Time Financial Information Searching with Large Language Models
  3. 2026FinRetrieval: A Benchmark for Financial Data Retrieval by AI Agents
  4. 2026CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering
  5. 2026FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use
  6. 2025FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation
  7. 2025FINCH: Financial Intelligence using Natural language for Contextualized SQL Handling
  8. 2024FinBen: A Holistic Financial Benchmark for Large Language Models