mlsanityData to 14 Oct 2026

Redefining Developer Assistance: Through Large Language Models in Software Ecosystem

arXiv 2312.05626 ↗First posted 9 Dec 2023cs.SE cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we delve into the advancement of domain-specific Large Language Models (LLMs) with a focus on their application in software development. We introduce DevAssistLlama, a model developed through instruction tuning, to assist developers in processing software-related natural language queries. This model, a variant of instruction tuned LLM, is particularly adept at handling intricate technical documentation, enhancing developer capability in software specific tasks. The creation of DevAssistLlama involved constructing an extensive instruction dataset from various software systems, enabling effective handling of Named Entity Recognition (NER), Relation Extraction (RE), and Link Prediction (LP). Our results demonstrate DevAssistLlama's superior capabilities in these tasks, in comparison with other models including ChatGPT. This research not only highlights the potential of specialized LLMs in software development also the pioneer LLM for this domain.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 9 Dec 2023.

Builds on

17 of 29 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2023Teaching Large Language Models to Self-Debugcited by 988
  6. 2023An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generationcited by 194
  7. 2022GitHub Copilot AI pair programmer: Asset or Liability?cited by 152
  8. 2023The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Developmentcited by 96
  9. 2023UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognitioncited by 72
  10. 2023LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Codingcited by 64
  11. 2023EcoAssistant: Using LLM Assistant More Affordably and Accuratelycited by 49
  12. 2020Code and Named Entity Recognition in StackOverflowcited by 39
5 more
  1. 2022PUnifiedNER: A Prompting-based Unified NER System for Diverse Datasetscited by 10
  2. 2021Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understandingcited by 5
  3. 2023Duplicate Question Retrieval and Confirmation Time Prediction in Software Communitiescited by 5
  4. 2023Software Entity Recognition with Noise-Robust Learningcited by 1
  5. 2023Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platformscited by 1

Built on by

Not cited yet.