The Human Factor in Detecting Errors of Large Language Models: A Systematic Literature Review and Future Research Directions
The launch of ChatGPT by OpenAI in November 2022 marked a pivotal moment for Artificial Intelligence, introducing Large Language Models (LLMs) to the mainstream and setting new records in user adoption. LLMs, particularly ChatGPT, trained on extensive internet data, demonstrate remarkable conversational capabilities across various domains, suggesting a significant impact on the workforce. However, these models are susceptible to errors - "hallucinations" and omissions, generating incorrect or incomplete information. This poses risks especially in contexts where accuracy is crucial, such as legal compliance, medicine or fine-grained process frameworks. There are both technical and human solutions to cope with this isse. This paper explores the human factors that enable users to detect errors in LLM outputs, a critical component in mitigating risks associated with their use in professional settings. Understanding these factors is essential for organizations aiming to leverage LLM technology efficiently, guiding targeted training and deployment strategies to enhance error detection by users. This approach not only aims to optimize the use of LLMs but also to prevent potential downstream issues stemming from reliance on inaccurate model responses. The research emphasizes the balance between technological advancement and human insight in maximizing the benefits of LLMs while minimizing the risks, particularly in areas where precision is paramount. This paper performs a systematic literature research on this research topic, analyses and synthesizes the findings, and outlines future research directions. Literature selection cut-off date is January 11th 2024.
Code (0)
등록된 구현이 없습니다.
Tasks
Systematic Literature ReviewSimilar Papers 제목 키워드 기반
Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual Errors
Accurate text summarization is one of the most common and important tasks performed by Large Language Models, where the costs of human review for an entire document may be high, but the costs of errors in summarization m…
HallucinationLanguage ModelingLanguage ModellingLarge Language Model+1Enhancing Grammatical Error Correction Systems with Explanations
Grammatical error correction systems improve written communication by detecting and correcting language mistakes. To help language learners better understand why the GEC system makes a certain correction, the causes of e…
Grammatical Error CorrectionDetecting over/under-translation errors for determining adequacy in human translations
We present a novel approach to detecting over and under translations (OT/UT) as part of adequacy error checks in translation evaluation. We do not restrict ourselves to machine translation (MT) outputs and specifically t…
Language ModelingLanguage ModellingMachine TranslationTranslationPreventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications. Especially for detecting humans, which is often a fundamental task in safety-critical app…
Human Detectionobject-detectionObject DetectionDetecting Label Errors by using Pre-Trained Language Models
We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task los…