# Generating Clarification Questions for Disambiguating Contracts

arXiv: https://arxiv.org/abs/2403.08053 · HTML: https://mlsanity.com/paper/2403.08053/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 27 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 13 of 13)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models](https://mlsanity.com/paper/2212.07769/) (2022)
- [PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents](https://mlsanity.com/paper/2305.14564/) (2023)
- [A Survey on Asking Clarification Questions Datasets in Conversational Systems](https://mlsanity.com/paper/2305.15933/) (2023)
- [Zero-shot Clarifying Question Generation for Conversational Search](https://mlsanity.com/paper/2301.12660/) (2023)
- [Automatic Detection of Vague Words and Sentences in Privacy Policies](https://mlsanity.com/paper/1808.06219/) (2018)
- [Diverse and Specific Clarification Question Generation with Keywords](https://mlsanity.com/paper/2104.10317/) (2021)
- [ConReader: Exploring Implicit Relations in Contracts for Contract Clause Extraction](https://mlsanity.com/paper/2210.08697/) (2022)
- [Ranking Clarification Questions via Natural Language Inference](https://mlsanity.com/paper/2008.07688/) (2020)
- [Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal Stakeholder's Perspective](https://mlsanity.com/paper/2312.01398/) (2023)

## Built on by (most cited first, 1 of 1)

- [LAURA: Knowledge Distillation for Interpretable Ambiguous Clause Identification in Legal Contracts](https://mlsanity.com/paper/2609.36707/) (2026)

## Abstract (arXiv)

Enterprises frequently enter into commercial contracts that can serve as vital sources of project-specific requirements. Contractual clauses are obligatory, and the requirements derived from contracts can detail the downstream implementation activities that non-legal stakeholders, including requirement analysts, engineers, and delivery personnel, need to conduct. However, comprehending contracts is cognitively demanding and error-prone for such stakeholders due to the extensive use of Legalese and the inherent complexity of contract language. Furthermore, contracts often contain ambiguously worded clauses to ensure comprehensive coverage. In contrast, non-legal stakeholders require a detailed and unambiguous comprehension of contractual clauses to craft actionable requirements. In this work, we introduce a novel legal NLP task that involves generating clarification questions for contracts. These questions aim to identify contract ambiguities on a document level, thereby assisting non-legal stakeholders in obtaining the necessary details for eliciting requirements. This task is challenged by three core issues: (1) data availability, (2) the length and unstructured nature of contracts, and (3) the complexity of legal text. To address these issues, we propose ConRAP, a retrieval-augmented prompting framework for generating clarification questions to disambiguate contractual text. Experiments conducted on contracts sourced from the publicly available CUAD dataset show that ConRAP with ChatGPT can detect ambiguities with an F2 score of 0.87. 70% of the generated clarification questions are deemed useful by human evaluators.