# Reframing Tax Law Entailment as Analogical Reasoning

arXiv: https://arxiv.org/abs/2401.06715 · HTML: https://mlsanity.com/paper/2401.06715/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 0 of its 32 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)

## Built on by (most cited first, 2 of 2)

- [AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies](https://mlsanity.com/paper/2402.12370/) (2024)
- [Reasoners or Translators? Contamination-aware Evaluation and Neuro-Symbolic Robustness in Tax Law](https://mlsanity.com/paper/2605.16052/) (2026)

## Abstract (arXiv)

Statutory reasoning refers to the application of legislative provisions to a series of case facts described in natural language. We re-frame statutory reasoning as an analogy task, where each instance of the analogy task involves a combination of two instances of statutory reasoning. This increases the dataset size by two orders of magnitude, and introduces an element of interpretability. We show that this task is roughly as difficult to Natural Language Processing models as the original task. Finally, we come back to statutory reasoning, solving it with a combination of a retrieval mechanism and analogy models, and showing some progress on prior comparable work.