# LEVEN: A Large-Scale Chinese Legal Event Detection Dataset

arXiv: https://arxiv.org/abs/2203.08556 · HTML: https://mlsanity.com/paper/2203.08556/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 56 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts](https://mlsanity.com/paper/2110.01799/) (2021)
- [How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence](https://mlsanity.com/paper/2004.12158/) (2020)
- [JEC-QA: A Legal-Domain Question Answering Dataset](https://mlsanity.com/paper/1911.12011/) (2019)
- [Lawformer: A Pre-trained Language Model for Chinese Legal Long Documents](https://mlsanity.com/paper/2105.03887/) (2021)
- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [Question Answering for Privacy Policies: Combining Computational and Legal Perspectives](https://mlsanity.com/paper/1911.00841/) (2019)
- [Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://mlsanity.com/paper/1904.07535/) (2019)
- [Analogical Reasoning on Chinese Morphological and Semantic Relations](https://mlsanity.com/paper/1805.06504/) (2018)
- [Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network](https://mlsanity.com/paper/1905.03969/) (2019)
- [Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions](https://mlsanity.com/paper/1802.08504/) (2018)
- [CLEVE: Contrastive Pre-training for Event Extraction](https://mlsanity.com/paper/2105.14485/) (2021)
- [LexNLP: Natural language processing and information extraction for legal and regulatory texts](https://mlsanity.com/paper/1806.03688/) (2018)
- [On the Ethical Limits of Natural Language Processing on Legal Text](https://mlsanity.com/paper/2105.02751/) (2021)
- [Equality before the Law: Legal Judgment Consistency Analysis for Fairness](https://mlsanity.com/paper/2103.13868/) (2021)
- [Learning to Check Contract Inconsistencies](https://mlsanity.com/paper/2012.08150/) (2020)

## Built on by (most cited first, 30 of 36)

- [DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services](https://mlsanity.com/paper/2309.11325/) (2023)
- [LawBench: Benchmarking Legal Knowledge of Large Language Models](https://mlsanity.com/paper/2309.16289/) (2023)
- [A Multi-Task Benchmark for Korean Legal Language Understanding and Judgement Prediction](https://mlsanity.com/paper/2206.05224/) (2022)
- [The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation](https://mlsanity.com/paper/2306.06918/) (2023)
- [SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding](https://mlsanity.com/paper/2308.10529/) (2023)
- [Learning Interpretable Legal Case Retrieval via Knowledge-Guided Case Reformulation](https://mlsanity.com/paper/2406.19760/) (2024)
- [PLawBench: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice](https://mlsanity.com/paper/2601.16669/) (2026)
- [Ready Jurist One: Benchmarking Language Agents for Legal Intelligence in Dynamic Environments](https://mlsanity.com/paper/2507.04037/) (2025)
- [OmniEvent: A Comprehensive, Fair, and Easy-to-Use Toolkit for Event Understanding](https://mlsanity.com/paper/2309.14258/) (2023)
- [Large Language Models Meet Legal Artificial Intelligence: A Survey](https://mlsanity.com/paper/2509.09969/) (2025)
- [Event Grounded Criminal Court View Generation with Cooperative (Large) Language Models](https://mlsanity.com/paper/2404.07001/) (2024)
- [J&H: Evaluating the Robustness of Large Language Models Under Knowledge-Injection Attacks in Legal Domain](https://mlsanity.com/paper/2503.18360/) (2025)
- [Legal Mathematical Reasoning with LLMs: Procedural Alignment through Two-Stage Reinforcement Learning](https://mlsanity.com/paper/2504.02590/) (2025)
- [InternLM-Law: An Open Source Chinese Legal Large Language Model](https://mlsanity.com/paper/2406.14887/) (2024)
- [Event Extraction in Large Language Model](https://mlsanity.com/paper/2512.19537/) (2025)
- [LEEC: A Legal Element Extraction Dataset with an Extensive Domain-Specific Label System](https://mlsanity.com/paper/2310.01271/) (2023)
- [Developing a Pragmatic Benchmark for Assessing Korean Legal Language Understanding in Large Language Models](https://mlsanity.com/paper/2410.08731/) (2024)
- [TestAgent: Automatic Benchmarking and Exploratory Interaction for Evaluating LLMs in Vertical Domains](https://mlsanity.com/paper/2410.11507/) (2024)
- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [Data-efficient End-to-end Information Extraction for Statistical Legal Analysis](https://mlsanity.com/paper/2211.01692/) (2022)
- [LegalBench.PT: A Benchmark for Portuguese Law](https://mlsanity.com/paper/2502.16357/) (2025)
- [Computational Law: Datasets, Benchmarks, and Ontologies](https://mlsanity.com/paper/2503.04305/) (2025)
- [LexRubric: A Rubric-Guided Diagnostic Benchmark for Open-Ended Legal Tasks](https://mlsanity.com/paper/2606.09389/) (2026)
- [EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain](https://mlsanity.com/paper/2406.14075/) (2024)
- [Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment](https://mlsanity.com/paper/2410.09083/) (2024)
- [CAPC-CG: A Large-Scale, Expert-Directed LLM-Annotated Corpus of Adaptive Policy Communication in China](https://mlsanity.com/paper/2510.08986/) (2025)
- [Evaluation of Large Language Models in Legal Applications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2601.15267/) (2026)
- [FAIR: A Causal Framework for Accurately Inferring Judgments Reversals](https://mlsanity.com/paper/2306.11585/) (2023)
- [NESTLE: a No-Code Tool for Statistical Analysis of Legal Corpus](https://mlsanity.com/paper/2309.04146/) (2023)
- [The Ethics of Automating Legal Actors](https://mlsanity.com/paper/2312.00584/) (2023)

## Abstract (arXiv)

Recognizing facts is the most fundamental step in making judgments, hence detecting events in the legal documents is important to legal case analysis tasks. However, existing Legal Event Detection (LED) datasets only concern incomprehensive event types and have limited annotated data, which restricts the development of LED methods and their downstream applications. To alleviate these issues, we present LEVEN a large-scale Chinese LEgal eVENt detection dataset, with 8,116 legal documents and 150,977 human-annotated event mentions in 108 event types. Not only charge-related events, LEVEN also covers general events, which are critical for legal case understanding but neglected in existing LED datasets. To our knowledge, LEVEN is the largest LED dataset and has dozens of times the data scale of others, which shall significantly promote the training and evaluation of LED methods. The results of extensive experiments indicate that LED is challenging and needs further effort. Moreover, we simply utilize legal events as side information to promote downstream applications. The method achieves improvements of average 2.2 points precision in low-resource judgment prediction, and 1.5 points mean average precision in unsupervised case retrieval, which suggests the fundamentality of LED. The source code and dataset can be obtained from https://github.com/thunlp/LEVEN.