# On the Role of Negative Precedent in Legal Outcome Prediction

arXiv: https://arxiv.org/abs/2208.08225 · HTML: https://mlsanity.com/paper/2208.08225/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (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)
- [LEGAL-BERT: The Muppets straight out of Law School](https://mlsanity.com/paper/2010.02559/) (2020)
- [Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases](https://mlsanity.com/paper/2103.13084/) (2021)
- [Distinguish Confusing Law Articles for Legal Judgment Prediction](https://mlsanity.com/paper/2004.02557/) (2020)
- [What About the Precedent: An Information-Theoretic Analysis of Common Law](https://mlsanity.com/paper/2104.12133/) (2021)
- [The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification](https://mlsanity.com/paper/2109.07234/) (2021)

## Built on by (most cited first, 16 of 16)

- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [(A)I Am Not a Lawyer, But...: Engaging Legal Experts towards Responsible LLM Policies for Legal Advice](https://mlsanity.com/paper/2402.01864/) (2024)
- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://mlsanity.com/paper/2212.02199/) (2022)
- [How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions](https://mlsanity.com/paper/2406.14805/) (2024)
- [From Dissonance to Insights: Dissecting Disagreements in Rationale Construction for Case Outcome Classification](https://mlsanity.com/paper/2310.11878/) (2023)
- [Leveraging Task Dependency and Contrastive Learning for Case Outcome Classification on European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00768/) (2023)
- [ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks](https://mlsanity.com/paper/2405.14211/) (2024)
- [Japanese Tort-case Dataset for Rationale-supported Legal Judgment Prediction](https://mlsanity.com/paper/2312.00480/) (2023)
- [Towards Explainability in Legal Outcome Prediction Models](https://mlsanity.com/paper/2403.16852/) (2024)
- [Incorporating Precedents for Legal Judgement Prediction on European Court of Human Rights Cases](https://mlsanity.com/paper/2409.18644/) (2024)
- [AnnoCaseLaw: A Richly-Annotated Dataset For Benchmarking Explainable Legal Judgment Prediction](https://mlsanity.com/paper/2503.00128/) (2025)
- [Team UTSA-NLP at SemEval 2024 Task 5: Prompt Ensembling for Argument Reasoning in Civil Procedures with GPT4](https://mlsanity.com/paper/2404.01961/) (2024)
- [The Craft of Selective Prediction: Towards Reliable Case Outcome Classification -- An Empirical Study on European Court of Human Rights Cases](https://mlsanity.com/paper/2409.18645/) (2024)
- [The Ethics of Automating Legal Actors](https://mlsanity.com/paper/2312.00584/) (2023)
- [BayesJudge: Bayesian Kernel Language Modelling with Confidence Uncertainty in Legal Judgment Prediction](https://mlsanity.com/paper/2404.10481/) (2024)
- [Towards Supporting Legal Argumentation with NLP: Is More Data Really All You Need?](https://mlsanity.com/paper/2406.10974/) (2024)

## Abstract (arXiv)

Every legal case sets a precedent by developing the law in one of the following two ways. It either expands its scope, in which case it sets positive precedent, or it narrows it, in which case it sets negative precedent. Legal outcome prediction, the prediction of positive outcome, is an increasingly popular task in AI. In contrast, we turn our focus to negative outcomes here, and introduce a new task of negative outcome prediction. We discover an asymmetry in existing models' ability to predict positive and negative outcomes. Where the state-of-the-art outcome prediction model we used predicts positive outcomes at 75.06 F1, it predicts negative outcomes at only 10.09 F1, worse than a random baseline. To address this performance gap, we develop two new models inspired by the dynamics of a court process. Our first model significantly improves positive outcome prediction score to 77.15 F1 and our second model more than doubles the negative outcome prediction performance to 24.01 F1. Despite this improvement, shifting focus to negative outcomes reveals that there is still much room for improvement for outcome prediction models.