Summary of Mitigating Hallucinations Using Ensemble Of Knowledge Graph and Vector Store in Large Language Models to Enhance Mental Health Support, by Abdul Muqtadir et al.
Mitigating Hallucinations Using Ensemble of Knowledge Graph and Vector Store in Large Language Models to Enhance Mental Health Support
by Abdul Muqtadir, Hafiz Syed Muhammad Bilal, Ayesha Yousaf, Hafiz Farooq Ahmed, Jamil Hussain
First submitted to arxiv on: 6 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates hallucinations in Large Language Models (LLMs) and their effects on applications in mental health. The goal is to find effective strategies for reducing hallucinations, making LLMs more dependable and secure for therapy, counseling, and disseminating information. By analyzing the underlying mechanisms of hallucinations, the study proposes targeted interventions to mitigate their occurrence. This research aims to create a robust framework for using LLMs in mental health contexts, ensuring their efficacy and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how Large Language Models (LLMs) can have “hallucinations” that affect how they’re used in mental health. The researchers want to stop these hallucinations from happening so the models are more reliable for helping with therapy, counseling, and sharing important information. They’ll look at why this happens and suggest ways to fix it. |
