Summary of Knowledgeprompts: Exploring the Abilities Of Large Language Models to Solve Proportional Analogies Via Knowledge-enhanced Prompting, by Thilini Wijesiriwardene and Ruwan Wickramarachchi and Sreeram Vennam and Vinija Jain and Aman Chadha and Amitava Das and Ponnurangam Kumaraguru and Amit Sheth
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
by Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam, Vinija Jain, Aman Chadha, Amitava Das, Ponnurangam Kumaraguru, Amit Sheth
First submitted to arxiv on: 1 Dec 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 Medium Difficulty Summary: This research paper introduces a new dataset, called 15K MCQA, which contains multiple-choice questions that require proportional analogy completion. The authors evaluate the performance of Large Language Models (LLMs) in completing these analogies, using various knowledge-enhanced prompt settings. Specifically, they provide LLMs with three types of knowledge: exemplar, structured, and targeted. The results show that current LLMs struggle to complete proportional analogies accurately, with a best model achieving 55% accuracy. Interestingly, the authors find that providing targeted knowledge is more effective in helping models complete these analogies compared to other types of knowledge. This paper contributes to the development of language models by highlighting the challenges they face when completing proportional analogies and suggesting ways to improve their performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Imagine trying to solve a puzzle where you have to find connections between words. This is what proportional analogy completion is all about! In this study, researchers created a huge dataset of puzzles like this and tested how well computers can solve them. They found that even the best computers struggle to get these answers right. But, when they gave the computers hints about the connections they were looking for, it helped them do better. This research helps us understand how computers can improve at solving problems like this, which is important for things like language translation and understanding human language. |
Keywords
» Artificial intelligence » Prompt » Translation
