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Understanding and Mitigating Large Language Model Hallucinations

Publisher IDC
Published Mar 29, 2024
Length 9 Pages
SKU # IDC18658501

Description

Understanding and Mitigating Large Language Model Hallucinations


This IDC Market Perspective analyzes the sources and various mitigation techniques and solutions for large language model (LLM) hallucinations, such as when using GenAI. Hallucinations where the model returns incorrect or misleading results in response to a prompt, and IDC expects that as more businesses adopt GenAI, those businesses will be faced with more and more significant hallucination issues that they are looking to technology vendors to solve. The increasing adoption of LLMs has resulted in an increase in the number of hallucinations that businesses are being forced to deal with that erode trust in the technology and in the responses it provides. Even with the disclaimers, the increasing importance of this technology and the vulnerability it causes for businesses are forcing researchers and technology suppliers to respond. Until we have a viable technology solution, businesses and technology suppliers will have to rely on a combination of sets of solutions for addressing the hallucination issue with model training and behavior, the underlying data and data sets, the engagement point between people and model, and model-specific issues."As almost every business is adopting GenAI and other LLMs, hallucinations are a real problem for businesses that can have significant impacts," said Alan Webber, program vice president, Digital Platform Ecosystems at IDC. "It is critical for technology suppliers to address the hallucination issue if they want and expect to maintain trust with their customers."

Please Note: Extended description available upon request.

Table of Contents

9 Pages
Executive Snapshot
New Market Developments and Dynamics
Types of LLM Hallucinations
Causes of LLM Hallucinations
Model Training Issues
Data Issues
Ways to Mitigate LLM Hallucinations
Model Training and Behavior-Focused Mitigation
Data-Focused Mitigation
People-Focused Mitigation Efforts
Model-Orientated Mitigation Efforts
LLM Self-Refinement
Employing RAG as a Mitigation Tool
Interesting Vendor Efforts to Mitigate LLM Hallucinations
Advice for the Technology Supplier
Learn More
Related Research
Synopsis

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