FinDialogLens：面向金融聊天室中错失交易识别的多方对话事件抽取
FinDialogLens: Event Extraction over Multi-Party Dialogue for Missed-Trade Identification in Financial Chatrooms
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中文总结 AI 辅助
针对金融聊天室错失交易识别，提出FinDialogLens混合LLM流水线，结合微调分类器与路由机制，实现高准确率并降低85%LLM调用成本。
中文摘要 AI 辅助
多方金融聊天室对销售与交易专业人士至关重要，但其复杂性使得手动恢复错失交易不可行：每个报价请求（RFQ）是一个事件，其最终价格和交易结果出现在RFQ触发消息（询价消息）之后的许多消息中，并与来自其他参与者的并发RFQ交织在一起。我们将此视为多方对话上的事件抽取（EE），并提出FinDialogLens，一个混合LLM流水线，其中紧凑的微调分类器作为推理时的脚手架：它们检测RFQ触发器和价格/交易结果元数据，一个RFQ级别模块分割每个事件的RFQ窗口，交易引擎填充参数角色。使用GPT-4o，FinDialogLens在最终价格和交易结果上分别达到92.1%和94.3%的准确率，优于全聊天室CoT提示方法；微调的开源LLM（参数少至3B）在适度的领域内数据下即可达到可比性能。为了使基于LLM的解决方案在大规模下实用，一个难度感知路由器通过在低成本基于规则的引擎和性能更高的LLM驱动的交易引擎之间分配RFQ来平衡成本与准确性，在最终价格上将LLM调用减少85%，同时恢复与FinDialogLens（GPT-4o）之间一半的准确率差距，在我们每天70,000个RFQ的规模下每天节省超过300美元。
英文摘要
Multi-party financial chatrooms are vital for sales-and-trading professionals, but their complexity makes manual recovery of missed trades infeasible: each Request for Quote (RFQ) is an event whose final price and trade outcome appear many messages after the RFQ-trigger message (the inquiry message), interleaved with concurrent RFQs from other participants. We cast this as event extraction (EE) over multi-party dialogue and present FinDialogLens, a hybrid LLM pipeline in which compact fine-tuned classifiers act as inference-time scaffolds: they detect RFQ-triggers and price/trade outcome metadata, an RFQ-Level Module segments per-event RFQ windows, and a Trade Engine fills argument roles. With GPT-4o, FinDialogLens reaches 92.1% and 94.3% accuracy on final price and trade outcome, respectively, outperforming full-chatroom CoT prompting methods; fine-tuned open-source LLMs with as few as 3B parameters achieve comparable performance with modest in-domain data. To make the LLM-based solution practical at scale, a difficulty-aware router balances cost and accuracy by allocating RFQs between a low-cost rule-based engine and the higher-performing LLM-powered Trade Engine, cutting LLM calls by 85% on final price while recovering half of the accuracy gap to FinDialogLens (GPT-4o), saving over $300/day at our 70,000-RFQ/day scale.
发表机构
- Machine Learning Center of Excellence, JPMorgan Chase & Co.（摩根大通机器学习卓越中心）
机构由 AI 辅助整理，请以论文原文为准。
