Summary of Harmonic Reasoning in Large Language Models, by Anna Kruspe
Harmonic Reasoning in Large Language Models
by Anna Kruspe
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD)
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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 This research paper explores the capabilities of Large Language Models (LLMs) in musical reasoning tasks, specifically identifying notes from intervals and recognizing chords and scales. The study tests GPT-3.5 and GPT-4o on these tasks, revealing that while LLMs excel at note intervals, they struggle with more complex tasks like chord and scale recognition. The findings highlight the limitations of current LLM abilities and provide insights for improving their performance in both artistic and complex domains. Additionally, the paper offers an automatically generated benchmark dataset for the described tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how well Large Language Models (LLMs) can understand and reason with musical concepts like notes, intervals, chords, and scales. Researchers tested two types of LLMs, GPT-3.5 and GPT-4o, to see how they do on these tasks. The results show that the models are good at figuring out notes from intervals, but struggle when it comes to recognizing more complex musical structures like chords and scales. This means we need to improve our LLMs so they can think and work better in artistic and other areas. |
Keywords
» Artificial intelligence » Gpt
