Can LLMs Replace Manual Annotation of Software Engineering Artifacts?

• URL: http://arxiv.org/abs/2408.05534v1
• Date: Sat, 10 Aug 2024 12:30:01 GMT
• Title: Can LLMs Replace Manual Annotation of Software Engineering Artifacts?
• Authors: Toufique Ahmed, Premkumar Devanbu, Christoph Treude, Michael Pradel,
• Abstract summary: Large language models (LLMs) have recently started to demonstrate human-level performance in several areas. This paper explores the possibility of substituting costly human subjects with much cheaper LLM queries in evaluations of code and code-related artifacts. Our results show that replacing some human annotation effort with LLMs can produce inter-rater agreements equal or close to human-rater agreement.
• Score: 24.563167762241346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Experimental evaluations of software engineering innovations, e.g., tools and processes, often include human-subject studies as a component of a multi-pronged strategy to obtain greater generalizability of the findings. However, human-subject studies in our field are challenging, due to the cost and difficulty of finding and employing suitable subjects, ideally, professional programmers with varying degrees of experience. Meanwhile, large language models (LLMs) have recently started to demonstrate human-level performance in several areas. This paper explores the possibility of substituting costly human subjects with much cheaper LLM queries in evaluations of code and code-related artifacts. We study this idea by applying six state-of-the-art LLMs to ten annotation tasks from five datasets created by prior work, such as judging the accuracy of a natural language summary of a method or deciding whether a code change fixes a static analysis warning. Our results show that replacing some human annotation effort with LLMs can produce inter-rater agreements equal or close to human-rater agreement. To help decide when and how to use LLMs in human-subject studies, we propose model-model agreement as a predictor of whether a given task is suitable for LLMs at all, and model confidence as a means to select specific samples where LLMs can safely replace human annotators. Overall, our work is the first step toward mixed human-LLM evaluations in software engineering.

Related papers

• Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge in Software Engineering [18.766132076075365]
  Large language models (LLMs) have been deployed to tackle various software engineering (SE) tasks like code generation. Pass@k metric requires extensive unit tests and configured environments, and is not suitable for evaluating LLM-generated text. Conventional metrics like BLEU, which measure only lexical rather than semantic similarity, have also come under scrutiny.
  arXiv  Detail & Related papers  (2025-02-10T06:49:29Z)
• The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs [21.97227334180969]
  "LLM-as-a-judge" paradigm employs Large Language Models as annotators and evaluators in tasks traditionally performed by humans. Despite their role in shaping study results and insights, there is no standard or rigorous procedure to determine whether LLMs can replace human annotators. We propose a novel statistical procedure -- the Alternative Annotator Test (alt-test) -- that requires only a modest subset of annotated examples to justify using LLM annotations.
  arXiv  Detail & Related papers  (2025-01-19T07:09:11Z)
• Human-Instruction-Free LLM Self-Alignment with Limited Samples [64.69906311787055]
  We propose an algorithm that can self-align large language models (LLMs) iteratively without active human involvement. Unlike existing works, our algorithm relies on neither human-crafted instructions nor labeled rewards, significantly reducing human involvement. We show that our method can unlock the LLMs' self-generalization ability to perform alignment with near-zero human supervision.
  arXiv  Detail & Related papers  (2024-01-06T14:00:12Z)
• CLOMO: Counterfactual Logical Modification with Large Language Models [109.60793869938534]
  We introduce a novel task, Counterfactual Logical Modification (CLOMO), and a high-quality human-annotated benchmark. In this task, LLMs must adeptly alter a given argumentative text to uphold a predetermined logical relationship. We propose an innovative evaluation metric, the Self-Evaluation Score (SES), to directly evaluate the natural language output of LLMs.
  arXiv  Detail & Related papers  (2023-11-29T08:29:54Z)
• Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks [65.69651759036535]
  We analyze whether large language models (LLMs) can serve as reliable alternatives to humans. This paper explores both conventional tasks (e.g., story generation) and alignment tasks (e.g., math reasoning) We find that LLM evaluators can generate unnecessary criteria or omit crucial criteria, resulting in a slight deviation from the experts.
  arXiv  Detail & Related papers  (2023-10-30T17:04:35Z)
• LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis [18.775126929754833]
  Thematic analysis (TA) has been widely used for analyzing qualitative data in many disciplines and fields. Human coders develop and deepen their data interpretation and coding over multiple iterations, making TA labor-intensive and time-consuming. We propose a human-LLM collaboration framework (i.e., LLM-in-the-loop) to conduct TA with in-context learning (ICL)
  arXiv  Detail & Related papers  (2023-10-23T17:05:59Z)
• 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)
• Artificial Artificial Artificial Intelligence: Crowd Workers Widely Use Large Language Models for Text Production Tasks [12.723777984461693]
  Large language models (LLMs) are remarkable data annotators. Crowdsourcing, an important, inexpensive way to obtain human annotations, may itself be impacted by LLMs. We estimate that 33-46% of crowd workers used LLMs when completing a task.
  arXiv  Detail & Related papers  (2023-06-13T16:46:24Z)
• Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization [66.08074487429477]
  We investigate the stability and reliability of large language models (LLMs) as automatic evaluators for abstractive summarization. We find that while ChatGPT and GPT-4 outperform the commonly used automatic metrics, they are not ready as human replacements.
  arXiv  Detail & Related papers  (2023-05-22T14:58:13Z)
• Can Large Language Models Be an Alternative to Human Evaluations? [80.81532239566992]
  Large language models (LLMs) have demonstrated exceptional performance on unseen tasks when only the task instructions are provided. We show that the result of LLM evaluation is consistent with the results obtained by expert human evaluation.
  arXiv  Detail & Related papers  (2023-05-03T07:28:50Z)

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.