# Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AI

arXiv: https://arxiv.org/abs/2402.16977 · HTML: https://mlsanity.com/paper/2402.16977/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 66 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Survey](https://mlsanity.com/paper/2305.18703/) (2023)
- [CLAUDETTE: an Automated Detector of Potentially Unfair Clauses in Online Terms of Service](https://mlsanity.com/paper/1805.01217/) (2018)
- [Requirements Engineering for Machine Learning: Perspectives from Data Scientists](https://mlsanity.com/paper/1908.04674/) (2019)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [Requirements Engineering for Artificial Intelligence Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2212.10693/) (2022)
- [A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis](https://mlsanity.com/paper/2301.08826/) (2023)
- [Agent-Specific Deontic Modality Detection in Legal Language](https://mlsanity.com/paper/2211.12752/) (2022)
- [On the Evaluation of NLP-based Models for Software Engineering](https://mlsanity.com/paper/2203.17166/) (2022)
- [A Machine Learning Approach for Hierarchical Classification of Software Requirements](https://mlsanity.com/paper/2302.12599/) (2023)
- [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, 2 of 2)

- [Multi-Sample Prompting and Actor-Critic Prompt Optimization for Diverse Synthetic Data Generation](https://mlsanity.com/paper/2506.21138/) (2025)
- [Reconsidering Requirements Engineering: Human-AI Collaboration in AI-Native Software Development](https://mlsanity.com/paper/2510.04380/) (2025)

## Abstract (arXiv)

Across the dynamic business landscape today, enterprises face an ever-increasing range of challenges. These include the constantly evolving regulatory environment, the growing demand for personalization within software applications, and the heightened emphasis on governance. In response to these multifaceted demands, large enterprises have been adopting automation that spans from the optimization of core business processes to the enhancement of customer experiences. Indeed, Artificial Intelligence (AI) has emerged as a pivotal element of modern software systems. In this context, data plays an indispensable role. AI-centric software systems based on supervised learning and operating at an industrial scale require large volumes of training data to perform effectively. Moreover, the incorporation of generative AI has led to a growing demand for adequate evaluation benchmarks. Our experience in this field has revealed that the requirement for large datasets for training and evaluation introduces a host of intricate challenges. This book chapter explores the evolving landscape of Software Engineering (SE) in general, and Requirements Engineering (RE) in particular, in this era marked by AI integration. We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems. The chapter provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary for effectively building solutions with NLP at their cores. We also reflect on how these text data-centric tasks sit together with the traditional RE process. We also highlight new RE tasks that may be necessary for handling the increasingly important text data-centricity involved in developing software systems.