The Career Interests of Large Language Models
- URL: http://arxiv.org/abs/2407.08564v1
- Date: Thu, 11 Jul 2024 14:54:46 GMT
- Title: The Career Interests of Large Language Models
- Authors: Meng Hua, Yuan Cheng, Hengshu Zhu,
- Abstract summary: This study focuses on the aspect of career interests by applying the Occupation Network's Interest Profiler short form to Large Language Models (LLMs)
We found distinct career interest inclinations among LLMs, particularly towards the social and artistic domains.
This novel approach of using psychometric instruments and sophisticated statistical tools on LLMs unveils fresh perspectives on their integration into professional environments.
- Score: 35.18062121410434
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent advancements in Large Language Models (LLMs) have significantly extended their capabilities, evolving from basic text generation to complex, human-like interactions. In light of the possibilities that LLMs could assume significant workplace responsibilities, it becomes imminently necessary to explore LLMs' capacities as professional assistants. This study focuses on the aspect of career interests by applying the Occupation Network's Interest Profiler short form to LLMs as if they were human participants and investigates their hypothetical career interests and competence, examining how these vary with language changes and model advancements. We analyzed the answers using a general linear mixed model approach and found distinct career interest inclinations among LLMs, particularly towards the social and artistic domains. Interestingly, these preferences did not align with the occupations where LLMs exhibited higher competence. This novel approach of using psychometric instruments and sophisticated statistical tools on LLMs unveils fresh perspectives on their integration into professional environments, highlighting human-like tendencies and promoting a reevaluation of LLMs' self-perception and competency alignment in the workforce.
Related papers
- Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics [0.46174569259495524]
This survey paper outlines the key developments in the field of Large Language Models (LLMs)<n>The techniques that have been most effective in bridging the gap between human and machine communications include the Chain-of-Thought prompting, Instruction Tuning, and Reinforcement Learning from Human Feedback.<n>A significant focus is placed on efficiency, detailing scaling strategies, optimization techniques, and the influential Mixture-of-Experts (MoE) architecture.
arXiv Detail & Related papers (2025-06-14T05:55:19Z) - Bridging Language Models and Financial Analysis [49.361943182322385]
The rapid advancements in Large Language Models (LLMs) have unlocked transformative possibilities in natural language processing.
Financial data is often embedded in intricate relationships across textual content, numerical tables, and visual charts.
Despite the fast pace of innovation in LLM research, there remains a significant gap in their practical adoption within the finance industry.
arXiv Detail & Related papers (2025-03-14T01:35:20Z) - An Overview of Large Language Models for Statisticians [109.38601458831545]
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)
This paper explores potential areas where statisticians can make important contributions to the development of LLMs.
We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
arXiv Detail & Related papers (2025-02-25T03:40:36Z) - Position: LLMs Can be Good Tutors in Foreign Language Education [87.88557755407815]
We argue that large language models (LLMs) have the potential to serve as effective tutors in foreign language education (FLE)
Specifically, LLMs can play three critical roles: (1) as data enhancers, improving the creation of learning materials or serving as student simulations; (2) as task predictors, serving as learner assessment or optimizing learning pathway; and (3) as agents, enabling personalized and inclusive education.
arXiv Detail & Related papers (2025-02-08T06:48:49Z) - A Survey on Large Language Models with some Insights on their Capabilities and Limitations [0.3222802562733786]
Large Language Models (LLMs) exhibit remarkable performance across various language-related tasks.
LLMs have demonstrated emergent abilities extending beyond their core functions.
This paper explores the foundational components, scaling mechanisms, and architectural strategies that drive these capabilities.
arXiv Detail & Related papers (2025-01-03T21:04:49Z) - Causality for Large Language Models [37.10970529459278]
Large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks.
Recent research highlights that LLMs function as causal parrots, capable of reciting causal knowledge without truly understanding or applying it.
This survey aims to explore how causality can enhance LLMs at every stage of their lifecycle.
arXiv Detail & Related papers (2024-10-20T07:22:23Z) - Transforming Scholarly Landscapes: Influence of Large Language Models on Academic Fields beyond Computer Science [77.31665252336157]
Large Language Models (LLMs) have ushered in a transformative era in Natural Language Processing (NLP)
This work empirically examines the influence and use of LLMs in fields beyond NLP.
arXiv Detail & Related papers (2024-09-29T01:32:35Z) - A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law.
We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies.
We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
arXiv Detail & Related papers (2024-05-02T22:43:02Z) - History, Development, and Principles of Large Language Models-An Introductory Survey [15.875687167037206]
Language models serve as a cornerstone in natural language processing (NLP)
Over extensive research spanning decades, language modeling has progressed from initial statistical language models (SLMs) to the contemporary landscape of large language models (LLMs)
arXiv Detail & Related papers (2024-02-10T01:18:15Z) - Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks.
The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human.
These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
arXiv Detail & Related papers (2024-01-30T17:38:54Z) - Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering.
The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored.
We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
arXiv Detail & Related papers (2023-12-26T07:24:46Z) - Large Language Models Meet Computer Vision: A Brief Survey [0.0]
Large Language Models (LLMs) and Computer Vision (CV) have emerged as a pivotal area of research, driving significant advancements in the field of Artificial Intelligence (AI)
This survey paper delves into the latest progressions in the domain of transformers, emphasizing their potential to revolutionize Vision Transformers (ViTs) and LLMs.
The survey is concluded by highlighting open directions in the field, suggesting potential venues for future research and development.
arXiv Detail & Related papers (2023-11-28T10:39:19Z) - LUNA: A Model-Based Universal Analysis Framework for Large Language Models [19.033382204019667]
Self-attention mechanism, extremely large model scale, and autoregressive generation schema present new challenges for quality analysis.
We propose a universal analysis framework for LLMs, designed to be general andinterpretable.
In particular, we first leverage the data from desired trustworthiness perspectives to construct an abstract model.
arXiv Detail & Related papers (2023-10-22T07:26:21Z) - A Survey on Large Language Model based Autonomous Agents [105.2509166861984]
Large language models (LLMs) have demonstrated remarkable potential in achieving human-level intelligence.
This paper delivers a systematic review of the field of LLM-based autonomous agents from a holistic perspective.
We present a comprehensive overview of the diverse applications of LLM-based autonomous agents in the fields of social science, natural science, and engineering.
arXiv Detail & Related papers (2023-08-22T13:30:37Z)
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.