Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?

• URL: http://arxiv.org/abs/2410.16285v1
• Date: Sat, 05 Oct 2024 14:37:35 GMT
• Title: Assessing the Performance of Human-Capable LLMs -- Are LLMs Coming for Your Job?
• Authors: John Mavi, Nathan Summers, Sergio Coronado,
• Abstract summary: SelfScore is a benchmark designed to assess the performance of automated Large Language Model (LLM) agents on help desk and professional consultation tasks. The benchmark evaluates agents on problem complexity and response helpfulness, ensuring transparency and simplicity in its scoring system. The study raises concerns about the potential displacement of human workers, especially in areas where AI technologies excel.
• Score: 0.0
• License:
• Abstract: The current paper presents the development and validation of SelfScore, a novel benchmark designed to assess the performance of automated Large Language Model (LLM) agents on help desk and professional consultation tasks. Given the increasing integration of AI in industries, particularly within customer service, SelfScore fills a crucial gap by enabling the comparison of automated agents and human workers. The benchmark evaluates agents on problem complexity and response helpfulness, ensuring transparency and simplicity in its scoring system. The study also develops automated LLM agents to assess SelfScore and explores the benefits of Retrieval-Augmented Generation (RAG) for domain-specific tasks, demonstrating that automated LLM agents incorporating RAG outperform those without. All automated LLM agents were observed to perform better than the human control group. Given these results, the study raises concerns about the potential displacement of human workers, especially in areas where AI technologies excel. Ultimately, SelfScore provides a foundational tool for understanding the impact of AI in help desk environments while advocating for ethical considerations in the ongoing transition towards automation.

Related papers

• AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
  We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
  arXiv  Detail & Related papers  (2024-11-02T13:24:30Z)
• AutoPenBench: Benchmarking Generative Agents for Penetration Testing [42.681170697805726]
  This paper introduces AutoPenBench, an open benchmark for evaluating generative agents in automated penetration testing. We present a comprehensive framework that includes 33 tasks, each representing a vulnerable system that the agent has to attack. We show the benefits of AutoPenBench by testing two agent architectures: a fully autonomous and a semi-autonomous supporting human interaction.
  arXiv  Detail & Related papers  (2024-10-04T08:24:15Z)
• WorkArena++: Towards Compositional Planning and Reasoning-based Common Knowledge Work Tasks [85.95607119635102]
  Large language models (LLMs) can mimic human-like intelligence. WorkArena++ is designed to evaluate the planning, problem-solving, logical/arithmetic reasoning, retrieval, and contextual understanding abilities of web agents.
  arXiv  Detail & Related papers  (2024-07-07T07:15:49Z)
• Position: A Call to Action for a Human-Centered AutoML Paradigm [83.78883610871867]
  Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) We argue that a key to unlocking AutoML's full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems.
  arXiv  Detail & Related papers  (2024-06-05T15:05:24Z)
• Auto-Arena: Automating LLM Evaluations with Agent Peer Battles and Committee Discussions [77.66677127535222]
  Auto-Arena is an innovative framework that automates the entire evaluation process using LLM-powered agents. In our experiments, Auto-Arena shows a 92.14% correlation with human preferences, surpassing all previous expert-annotated benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T17:19:19Z)
• Characteristic AI Agents via Large Language Models [40.10858767752735]
  This research focuses on investigating the performance of Large Language Models in constructing characteristic AI agents. A dataset called Character100'' is built for this benchmark, comprising the most-visited people on Wikipedia for language models to role-play. The experimental results underscore the potential directions for further improvement in the capabilities of LLMs in constructing characteristic AI agents.
  arXiv  Detail & Related papers  (2024-03-19T02:25:29Z)
• AutoAct: Automatic Agent Learning from Scratch for QA via Self-Planning [54.47116888545878]
  AutoAct is an automatic agent learning framework for QA. It does not rely on large-scale annotated data and synthetic planning trajectories from closed-source models.
  arXiv  Detail & Related papers  (2024-01-10T16:57:24Z)
• TaskBench: Benchmarking Large Language Models for Task Automation [82.2932794189585]
  We introduce TaskBench, a framework to evaluate the capability of large language models (LLMs) in task automation. Specifically, task decomposition, tool selection, and parameter prediction are assessed. Our approach combines automated construction with rigorous human verification, ensuring high consistency with human evaluation.
  arXiv  Detail & Related papers  (2023-11-30T18:02:44Z)
• Towards LLM-based Autograding for Short Textual Answers [4.853810201626855]
  This manuscript is an evaluation of a large language model for the purpose of autograding. Our findings suggest that while "out-of-the-box" LLMs provide a valuable tool, their readiness for independent automated grading remains a work in progress.
  arXiv  Detail & Related papers  (2023-09-09T22:25:56Z)
• Assessing the Use of AutoML for Data-Driven Software Engineering [10.40771687966477]
  AutoML promises to automate the building of end-to-end AI/ML pipelines. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted.
  arXiv  Detail & Related papers  (2023-07-20T11:14:24Z)

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.