Papers with IRAC
Chain of Logic: Rule-Based Reasoning with Large Language Models (2024.findings-acl)
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| Challenge: | Logic models are prone to hallucinations and are not able to perform basic tasks like drafting and drafting documents. |
| Approach: | They propose a new prompting method which elicits rule-based reasoning through decomposition and recomposition. |
| Outcome: | The proposed method outperforms other prompting methods including chain of thought and self-ask on eight rule-based reasoning tasks. |
Taxation Perspectives from Large Language Models: A Case Study on Additional Tax Penalties (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated promising results across various domains, including the legal domain. |
| Approach: | They propose a benchmark to assess the ability of large language models to predict the legitimacy of additional tax penalties. |
| Outcome: | The proposed model is based on 100 Korean court precedents and 100 binary-choice questions. |
Exploring the Effectiveness of Prompt Engineering for Legal Reasoning Tasks (2023.findings-acl)
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| Challenge: | Recent studies have shown that Chain-of-Thought (CoT) prompts improve tasks such as arithmetic and common-sense reasoning. |
| Approach: | They evaluate CoT prompts and various prompting strategies for legal reasoning tasks . they find that the best results are achieved with prompts derived from specific legal reasoning techniques . |
| Outcome: | The proposed approaches improve the COLIEE entailment task on the Japanese bar exam . the proposed approaches surpass the best system from 2022 with an accuracy of 0.789 . |