Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Developer Challenges on Large Language Models: A Study of Stack Overflow and OpenAI Developer Forum Posts

Khairul Alam, Kartik Mittal, Banani Roy, Chanchal Roy

TL;DR

It is found that LLM-related queries often exhibit great difficulty, with a substantial percentage of unresolved posts and prolonged response times, particularly for complex topics like 'Llama Indexing and GPU Utilization' and 'Agents and Tool Interactions'.

Abstract

Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains, including chatbots, healthcare, education, content generation, and automated support systems. However, developers encounter numerous challenges when implementing, fine-tuning, and integrating these models into real-world applications. This study investigates LLM developers' challenges by analyzing community interactions on Stack Overflow and OpenAI Developer Forum, employing BERTopic modeling to identify and categorize developer discussions. Our analysis yields nine challenges on Stack Overflow (e.g., LLM Ecosystem and Challenges, API Usage, LLM Training with Frameworks) and 17 on the OpenAI Developer Forum (e.g., API Usage and Error Handling, Fine-Tuning and Dataset Management). Results indicate that developers frequently turn to Stack Overflow for implementation guidance, while OpenAI's forum focuses on troubleshooting. Notably, API and functionality issues dominate discussions on the OpenAI forum, with many posts requiring multiple responses, reflecting the complexity of LLM-related problems. We find that LLM-related queries often exhibit great difficulty, with a substantial percentage of unresolved posts (e.g., 79.03\% on Stack Overflow) and prolonged response times, particularly for complex topics like 'Llama Indexing and GPU Utilization' and 'Agents and Tool Interactions'. In contrast, established fields like Mobile Development and Security enjoy quicker resolutions and stronger community engagement. These findings highlight the need for improved community support and targeted resources to assist LLM developers in overcoming the evolving challenges of this rapidly growing field. This study provides insights into areas of difficulty, paving the way for future research and tool development to better support the LLM developer community.

Developer Challenges on Large Language Models: A Study of Stack Overflow and OpenAI Developer Forum Posts

TL;DR

It is found that LLM-related queries often exhibit great difficulty, with a substantial percentage of unresolved posts and prolonged response times, particularly for complex topics like 'Llama Indexing and GPU Utilization' and 'Agents and Tool Interactions'.

Abstract

Large Language Models (LLMs) have gained widespread popularity due to their exceptional capabilities across various domains, including chatbots, healthcare, education, content generation, and automated support systems. However, developers encounter numerous challenges when implementing, fine-tuning, and integrating these models into real-world applications. This study investigates LLM developers' challenges by analyzing community interactions on Stack Overflow and OpenAI Developer Forum, employing BERTopic modeling to identify and categorize developer discussions. Our analysis yields nine challenges on Stack Overflow (e.g., LLM Ecosystem and Challenges, API Usage, LLM Training with Frameworks) and 17 on the OpenAI Developer Forum (e.g., API Usage and Error Handling, Fine-Tuning and Dataset Management). Results indicate that developers frequently turn to Stack Overflow for implementation guidance, while OpenAI's forum focuses on troubleshooting. Notably, API and functionality issues dominate discussions on the OpenAI forum, with many posts requiring multiple responses, reflecting the complexity of LLM-related problems. We find that LLM-related queries often exhibit great difficulty, with a substantial percentage of unresolved posts (e.g., 79.03\% on Stack Overflow) and prolonged response times, particularly for complex topics like 'Llama Indexing and GPU Utilization' and 'Agents and Tool Interactions'. In contrast, established fields like Mobile Development and Security enjoy quicker resolutions and stronger community engagement. These findings highlight the need for improved community support and targeted resources to assist LLM developers in overcoming the evolving challenges of this rapidly growing field. This study provides insights into areas of difficulty, paving the way for future research and tool development to better support the LLM developer community.

Paper Structure

This paper contains 19 sections, 2 equations, 6 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Large Language Models (LLMs)
  4. Prior Studies on LLMs
  5. Topic Modeling
  6. Topic Analysis of Technical Q&As
  7. Study Design
  8. Stack Overflow
  9. OpenAI Developer Forum
  10. Results
  11. RQ1: What topics are Large Language Model developers are asking about?
  12. RQ2: What types of questions are Large Language Model developers are asking?
  13. RQ3: To what extent do developers perceive the revealed challenges in terms of difficulties?
  14. Discussion & Implications of our Findings
  15. LLM Topics Evolution
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Overview of the methodology of our study
  • Figure 2: Types Distribution of SO Posts
  • Figure 3: Relative Growth of LLM-related posts over time in Stack Overflow
  • Figure 4: LLM Topic Evolution Over Time in Stack Overflow
  • Figure 5: Relative Growth of LLM-related posts over time in OpenAI Developer Forum
  • ...and 1 more figures