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发布 CalOn，一个 0.6B 参数的校准类型化决策模型，用于实体、关系与事件抽取，速度达每秒 120 句，是 GLiNER2 的 5.5 倍，并在 40 个基准上超越基线。权重、推理与训练代码均以 Apache 2.0 开源。
The paper presents FinDialogLens, a hybrid LLM pipeline for event extraction over multi-party financial chatrooms to recover missed trades, achieving 92.1% and 94.3% accuracy on final price and trade outcome with GPT-4o while cutting LLM calls by 85% via a difficulty-aware router.
EAGER is a reinforcement learning framework that improves generative event extraction through fine-grained verifiable rewards and schema-contrastive advantage estimation, outperforming prompting, fine-tuning, and prior RL methods on seven benchmark datasets.
LA-RL introduces a label-aware self-reflection framework for reinforcement learning in information extraction, achieving consistent improvements on named entity recognition, relation extraction, and event extraction tasks with gains of up to 20 F1 on out-of-distribution benchmarks.
This paper presents a two-stage LLM-based system that extracts grounded event tags from SEC 8-K filings using a three-tier taxonomy of 119 event types, with mechanisms for constraint and auditability. The system is evaluated on nearly 300k filings, showing high precision for high-quality tags and enabling event studies that distinguish economically distinct events.