Summary of Investigating the Role Of Prompting and External Tools in Hallucination Rates Of Large Language Models, by Liam Barkley and Brink Van Der Merwe
Investigating the Role of Prompting and External Tools in Hallucination Rates of Large Language Models
by Liam Barkley, Brink van der Merwe
First submitted to arxiv on: 25 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 Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) tasks, showcasing exceptional performance across various applications. However, these powerful models often produce inaccuracies, known as hallucinations, which can be detrimental to their overall efficacy. To mitigate this issue, prompt engineering has emerged as a crucial approach. This paper presents an extensive empirical evaluation of diverse prompting strategies and frameworks aimed at reducing hallucinations in LLMs. The study applies various prompting techniques to a broad range of benchmark datasets, assessing the accuracy and hallucination rate of each method. Additionally, it investigates how tool-calling agents (LLMs augmented with external tools) impact hallucination rates across the same benchmarks. The findings suggest that the optimal prompting technique depends on the specific problem type, and simpler methods often outperform more complex approaches in reducing hallucinations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine powerful computers called Language Models that can understand and generate human-like text. These models are great at many tasks, but sometimes they make mistakes, called “hallucinations”. To fix this, researchers came up with a new way to guide these models, called “prompt engineering”. This paper tests different ways to prompt language models to see which ones work best. It looks at how well the models do on various tasks and how often they make mistakes. The results show that some prompts are better than others, depending on what task you’re trying to accomplish. Surprisingly, using extra tools with these language models can sometimes even make things worse! |
Keywords
» Artificial intelligence » Hallucination » Natural language processing » Nlp » Prompt » Prompting
