Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks

• URL: http://arxiv.org/abs/2405.05135v1
• Date: Wed, 24 Apr 2024 20:26:48 GMT
• Title: Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks
• Authors: Mohamad Fazelnia, Viktoria Koscinski, Spencer Herzog, Mehdi Mirakhorli,
• Abstract summary: We investigate the use of Natural Language Inference (NLI) in automating requirements engineering tasks. In particular, we focus on three tasks: requirements classification, identification of requirements specification defects, and detection of conflicts in stakeholders' requirements.
• Score: 3.7236121078812485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We investigate the use of Natural Language Inference (NLI) in automating requirements engineering tasks. In particular, we focus on three tasks: requirements classification, identification of requirements specification defects, and detection of conflicts in stakeholders' requirements. While previous research has demonstrated significant benefit in using NLI as a universal method for a broad spectrum of natural language processing tasks, these advantages have not been investigated within the context of software requirements engineering. Therefore, we design experiments to evaluate the use of NLI in requirements analysis. We compare the performance of NLI with a spectrum of approaches, including prompt-based models, conventional transfer learning, Large Language Models (LLMs)-powered chatbot models, and probabilistic models. Through experiments conducted under various learning settings including conventional learning and zero-shot, we demonstrate conclusively that our NLI method surpasses classical NLP methods as well as other LLMs-based and chatbot models in the analysis of requirements specifications. Additionally, we share lessons learned characterizing the learning settings that make NLI a suitable approach for automating requirements engineering tasks.

Related papers

• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Scalable Language Model with Generalized Continual Learning [58.700439919096155]
  The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks. Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
  arXiv  Detail & Related papers  (2024-04-11T04:22:15Z)
• Practical Guidelines for the Selection and Evaluation of Natural Language Processing Techniques in Requirements Engineering [8.779031107963942]
  Natural language (NL) is now a cornerstone of requirements automation. With so many different NLP solution strategies available, it can be challenging to choose the right strategy for a specific RE task. In particular, we discuss how to choose among different strategies such as traditional NLP, feature-based machine learning, and language-model-based methods.
  arXiv  Detail & Related papers  (2024-01-03T02:24:35Z)
• Natural Language Processing for Requirements Formalization: How to Derive New Approaches? [0.32885740436059047]
  We present and discuss principal ideas and state-of-the-art methodologies from the field of NLP. We discuss two different approaches in detail and highlight the iterative development of rule sets. The presented methods are demonstrated on two industrial use cases from the automotive and railway domains.
  arXiv  Detail & Related papers  (2023-09-23T05:45:19Z)
• On Conditional and Compositional Language Model Differentiable Prompting [75.76546041094436]
  Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks. We propose a new model, Prompt Production System (PRopS), which learns to transform task instructions or input metadata, into continuous prompts.
  arXiv  Detail & Related papers  (2023-07-04T02:47:42Z)
• Skill-Based Few-Shot Selection for In-Context Learning [123.26522773708683]
  Skill-KNN is a skill-based few-shot selection method for in-context learning. It does not require training or fine-tuning of any models, making it suitable for frequently expanding or changing example banks. Experimental results across five cross-domain semantic parsing datasets and six backbone models show that Skill-KNN significantly outperforms existing methods.
  arXiv  Detail & Related papers  (2023-05-23T16:28:29Z)
• Requirement Formalisation using Natural Language Processing and Machine Learning: A Systematic Review [11.292853646607888]
  We conducted a systematic literature review to outline the current state-of-the-art of NLP and ML techniques in Requirement Engineering. We found that NLP approaches are the most common NLP techniques used for automatic RF, primary operating on structured and semi-structured data. This study also revealed that Deep Learning (DL) technique are not widely used, instead classical ML techniques are predominant in the surveyed studies.
  arXiv  Detail & Related papers  (2023-03-18T17:36:21Z)
• An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks [112.1942546460814]
  We report the first exploration of the prompt tuning paradigm for speech processing tasks based on Generative Spoken Language Model (GSLM) Experiment results show that the prompt tuning technique achieves competitive performance in speech classification tasks with fewer trainable parameters than fine-tuning specialized downstream models.
  arXiv  Detail & Related papers  (2022-03-31T03:26:55Z)
• AdaPrompt: Adaptive Model Training for Prompt-based NLP [77.12071707955889]
  We propose AdaPrompt, adaptively retrieving external data for continual pretraining of PLMs. Experimental results on five NLP benchmarks show that AdaPrompt can improve over standard PLMs in few-shot settings. In zero-shot settings, our method outperforms standard prompt-based methods by up to 26.35% relative error reduction.
  arXiv  Detail & Related papers  (2022-02-10T04:04:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.