Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks

• URL: http://arxiv.org/abs/2410.01026v1
• Date: Tue, 1 Oct 2024 19:34:46 GMT
• Title: Understanding the Human-LLM Dynamic: A Literature Survey of LLM Use in Programming Tasks
• Authors: Deborah Etsenake, Meiyappan Nagappan,
• Abstract summary: Large Language Models (LLMs) are transforming programming practices, offering significant capabilities for code generation activities. This paper focuses on their use in programming tasks, drawing insights from user studies that assess the impact of LLMs on programming tasks.
• Score: 0.850206009406913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are transforming programming practices, offering significant capabilities for code generation activities. While researchers have explored the potential of LLMs in various domains, this paper focuses on their use in programming tasks, drawing insights from user studies that assess the impact of LLMs on programming tasks. We first examined the user interaction behaviors with LLMs observed in these studies, from the types of requests made to task completion strategies. Additionally, our analysis reveals both benefits and weaknesses of LLMs showing mixed effects on the human and task. Lastly, we looked into what factors from the human, LLM or the interaction of both, affect the human's enhancement as well as the task performance. Our findings highlight the variability in human-LLM interactions due to the non-deterministic nature of both parties (humans and LLMs), underscoring the need for a deeper understanding of these interaction patterns. We conclude by providing some practical suggestions for researchers as well as programmers.

Related papers

• The LLM Effect: Are Humans Truly Using LLMs, or Are They Being Influenced By Them Instead? [60.01746782465275]
  Large Language Models (LLMs) have shown capabilities close to human performance in various analytical tasks. This paper investigates the efficiency and accuracy of LLMs in specialized tasks through a structured user study focusing on Human-LLM partnership.
  arXiv  Detail & Related papers  (2024-10-07T02:30:18Z)
• Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making [51.737762570776006]
  LLM-ACTR is a novel neuro-symbolic architecture that provides human-aligned and versatile decision-making. Our framework extracts and embeds knowledge of ACT-R's internal decision-making process as latent neural representations. Our experiments on novel Design for Manufacturing tasks show both improved task performance as well as improved grounded decision-making capability.
  arXiv  Detail & Related papers  (2024-08-17T11:49:53Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• Explaining Large Language Models Decisions with Shapley Values [1.223779595809275]
  Large language models (LLMs) have opened up exciting possibilities for simulating human behavior and cognitive processes. However, the validity of utilizing LLMs as stand-ins for human subjects remains uncertain. This paper presents a novel approach based on Shapley values to interpret LLM behavior and quantify the relative contribution of each prompt component to the model's output.
  arXiv  Detail & Related papers  (2024-03-29T22:49:43Z)
• Comparing Rationality Between Large Language Models and Humans: Insights and Open Questions [6.201550639431176]
  This paper focuses on the burgeoning prominence of large language models (LLMs) We underscore the pivotal role of Reinforcement Learning from Human Feedback (RLHF) in augmenting LLMs' rationality and decision-making prowess.
  arXiv  Detail & Related papers  (2024-03-14T18:36:04Z)
• Large Language Model-based Human-Agent Collaboration for Complex Task Solving [94.3914058341565]
  We introduce the problem of Large Language Models (LLMs)-based human-agent collaboration for complex task-solving. We propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC. This approach includes a policy model designed to determine the most opportune stages for human intervention within the task-solving process.
  arXiv  Detail & Related papers  (2024-02-20T11:03:36Z)
• An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Project [1.433758865948252]
  Large Language Models (LLMs) represent a leap in artificial intelligence, excelling in tasks using human language(s) In this paper, we analyze the AI-generated code, prompts used for code generation, and the human intervention levels to integrate the code into the code base. Our findings suggest that LLMs can play a crucial role in the early stages of software development.
  arXiv  Detail & Related papers  (2024-01-29T14:32:32Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs [25.4184470735779]
  LLMs have shown promise in replicating human-like behavior in crowdsourcing tasks that were previously thought to be exclusive to human abilities. We explore whether LLMs can replicate more complex crowdsourcing pipelines.
  arXiv  Detail & Related papers  (2023-07-19T17:54:43Z)
• LEMMA: A Multi-view Dataset for Learning Multi-agent Multi-task Activities [119.88381048477854]
  We introduce the LEMMA dataset to provide a single home to address missing dimensions with meticulously designed settings. We densely annotate the atomic-actions with human-object interactions to provide ground-truths of the compositionality, scheduling, and assignment of daily activities. We hope this effort would drive the machine vision community to examine goal-directed human activities and further study the task scheduling and assignment in the real world.
  arXiv  Detail & Related papers  (2020-07-31T00:13:54Z)

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.