Beyond Guilt: Legal Judgment Prediction with Trichotomous Reasoning (2025)

Abstract: In legal practice, judges apply the trichotomous dogmatics of criminal law, sequentially assessingthe elements of the offense, unlawfulness, and culpability to determine whether an individual’s conduct constitutes a crime. Although current legal large language models (LLMs) show promising accuracy in judgment prediction, they lack trichotomous reasoning capabilities due to the absence of an appropriate benchmark dataset, preventing them from predicting innocent outcomes. As a result, every input is automatically assigned a charge, limiting their practical utility in legal contexts. To bridge this gap, we introduce LJPIV, the first benchmark dataset for Legal Judgment Prediction with Innocent Verdicts. Adhering to the trichotomous dogmatics, we extend three widely-used legal datasets through LLM-based augmentation and manual verification. Our experiments with state-of-the-art legal LLMs and novel strategies that integrate trichotomous reasoning into zero-shot prompting and fine-tuning reveal: (1) current legal LLMs have significant room for improvement, with even the best models achieving an F1 score of less than 0.3 on LJPIV; and (2) our strategies notably enhance both in-domain and cross-domain judgment prediction accuracy, especially for cases resulting in an innocent verdict.

## introduction the trichotomous dogmatics @xcite is a key theory in criminal law used to determine whether an individual's conduct constitutes the elements of the offense unlawfulness culpability assess whether an individual's conduct objectively fulfills the criteria for a criminal offense. assess whether an individual has any grounds for justification, e.g., selfdefense. assess whether an individual acted with free will to be held responsible. ## dataset construction we focus on trichotomous reasoning for the legal judgment prediction task, which can be formulated as follows: given an input fact description x, the goal is to predict its judgment outcome @xmath0. unlike existing studies where y represents a set of criminal charges, we consider a more practical scenario by extending y to include a label for "innocent". specifically, we utilize a llm (qwen2-7b) to augment three popular ljp datasets and create a new benchmark, ljpiv, through a three-stage augmentation. ## trichotomous reasoning with llm in this section, we introduce both prompt-based and fine-tuning methods to enable llms to perform trichotomous reasoning for ljp. ## conclusion in this paper, we introduce ljpiv, the first benchmark dataset for legal judgment prediction with innocent verdicts. we extend three widely-used legal datasets through llm-based augmentation and manual verification. we further introduce zero-shot prompt-based and fine-tuning methods to equip open-domain llms with trichotomous reasoning capabilities, particularly for predicting innocent outcomes in legal judgment prediction. extensive experiments reveal that (1) current legal llms have significant room for improvement, with even the best models achieving an f1 score below 0.3 on ljpiv; (2) fine-tuning on ljpiv substantially improves both in-domain and cross-domain judgment prediction accuracy for open-domain llms, especially in cases resulting in an innocent verdict; and