Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline

• URL: http://arxiv.org/abs/2402.13823v3
• Date: Wed, 15 May 2024 12:57:58 GMT
• Title: Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline
• Authors: Andreas Vogelsang, Jannik Fischbach,
• Abstract summary: Large Language Models (LLMs) are the cornerstone in automating Requirements Engineering (RE) tasks. This chapter aims to furnish readers with essential knowledge about LLMs in its initial segment. It provides a comprehensive guideline tailored for students, researchers, and practitioners on harnessing LLMs to address their specific objectives.
• Score: 2.6644624823848426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are the cornerstone in automating Requirements Engineering (RE) tasks, underpinning recent advancements in the field. Their pre-trained comprehension of natural language is pivotal for effectively tailoring them to specific RE tasks. However, selecting an appropriate LLM from a myriad of existing architectures and fine-tuning it to address the intricacies of a given task poses a significant challenge for researchers and practitioners in the RE domain. Utilizing LLMs effectively for NLP problems in RE necessitates a dual understanding: firstly, of the inner workings of LLMs, and secondly, of a systematic approach to selecting and adapting LLMs for NLP4RE tasks. This chapter aims to furnish readers with essential knowledge about LLMs in its initial segment. Subsequently, it provides a comprehensive guideline tailored for students, researchers, and practitioners on harnessing LLMs to address their specific objectives. By offering insights into the workings of LLMs and furnishing a practical guide, this chapter contributes towards improving future research and applications leveraging LLMs for solving RE challenges.

Related papers

• RuAG: Learned-rule-augmented Generation for Large Language Models [62.64389390179651]
  We propose a novel framework, RuAG, to automatically distill large volumes of offline data into interpretable first-order logic rules. We evaluate our framework on public and private industrial tasks, including natural language processing, time-series, decision-making, and industrial tasks.
  arXiv  Detail & Related papers  (2024-11-04T00:01:34Z)
• A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks [0.0]
  Large language models (LLMs) have shown remarkable performance on many different Natural Language Processing (NLP) tasks. Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant performance gains. This paper summarizes different prompting techniques and club them together based on different NLP tasks that they have been used for.
  arXiv  Detail & Related papers  (2024-07-17T20:23:19Z)
• A Reality check of the benefits of LLM in business [1.9181612035055007]
  Large language models (LLMs) have achieved remarkable performance in language understanding and generation tasks. This paper thoroughly examines the usefulness and readiness of LLMs for business processes.
  arXiv  Detail & Related papers  (2024-06-09T02:36:00Z)
• A Survey of Useful LLM Evaluation [20.048914787813263]
  Two-stage framework: from core ability'' to agent'' In the "core ability" stage, we discussed the reasoning ability, societal impact, and domain knowledge of LLMs. In the agent'' stage, we demonstrated embodied action, planning, and tool learning of LLMs agent applications.
  arXiv  Detail & Related papers  (2024-06-03T02:20:03Z)
• Large Language Models Meet NLP: A Survey [79.74450825763851]
  Large language models (LLMs) have shown impressive capabilities in Natural Language Processing (NLP) tasks. This study aims to address this gap by exploring the following questions.
  arXiv  Detail & Related papers  (2024-05-21T14:24:01Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models. It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs [60.40396361115776]
  This paper introduces a novel collaborative approach, namely SlimPLM, that detects missing knowledge in large language models (LLMs) with a slim proxy model. We employ a proxy model which has far fewer parameters, and take its answers as answers. Heuristic answers are then utilized to predict the knowledge required to answer the user question, as well as the known and unknown knowledge within the LLM.
  arXiv  Detail & Related papers  (2024-02-19T11:11:08Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• LgTS: Dynamic Task Sampling using LLM-generated sub-goals for Reinforcement Learning Agents [10.936460061405157]
  We propose LgTS (LLM-guided Teacher-Student learning), a novel approach that explores the planning abilities of LLMs. Our approach does not assume access to a propreitary or a fine-tuned LLM, nor does it require pre-trained policies that achieve the sub-goals proposed by the LLM.
  arXiv  Detail & Related papers  (2023-10-14T00:07:03Z)
• Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond [48.70557995528463]
  This guide aims to provide researchers and practitioners with valuable insights and best practices for working with Large Language Models. We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios.
  arXiv  Detail & Related papers  (2023-04-26T17:52:30Z)

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.