File:Generating and modifying a tune in ABC notation with GPT-4.png

Original file(1,304 × 1,909 pixels, file size: 450 KB, MIME type: image/png)

Captions

Captions

From the preprint "Sparks of Artificial General Intelligence: Early experiments with GPT-4"

Summary edit

Description
English: "The data on which the model was trained also contains musical information encoded as ABC notation.

This is a system that uses letters, numbers and symbols to represent musical pitches, durations, chords and other elements in a compact and readable way. We are interested in exploring how well the model has acquired musical skills from this exposure, such as composing new melodies, transforming existing ones, and understanding musical patterns and structures. When instructed to generate a short tune (Figure 2.9), and the model was able to produce valid ABC notation. The tune had a clear structure, the time signature was consistent between bars and the notes followed increasing and decreasing patterns. The tune also used a consistent set of notes within the melody, and the rhythm had a repetitive pattern. However, the model did not seem to obtain the skill of understanding harmony. In fact, consecutive notes in the generated tunes are almost always adjacent to each other (namely, the note following C will almost typically be either B or D), and testing on 10 generated tunes, we were not able to extract any clear chords or arpeggios. We then asked the model to describe the tune in musical terms. It was able to successfully give a technical description of the structure in terms of repetitions, the rising or descending parts of the melody and to some extent the rhythm. However, it seems that the descriptions of the harmony and chords are not consistent with the notes (in fact, it refers to sequences of adjacent notes, which do not form valid chords, as arpeggios). We then asked the model to manipulate the melody in two ways. First, we instructed to change a certain rising sequence to a descending one, which it did successfully. Then we asked the model to convert the tune to a duet adding a bass voice. The model successfully extends the ABC notation with a second staff which has compatible rhythm and is played on a lower octave, however there is a lack of harmony between the two voices. In summary, the model was able to produce valid tunes in ABC notation and, to some extent, explain and manipulate their structure. However, we could not get the model to produce any nontrivial form of harmony. It should be noted that ABC notation is not a very widely used format, and in fact the model was not able to produce even the most well-known tunes in ABC notation (such as Ode to Joy, F ̈ur Elise or Greensleeves,

all of which are abundant online in that format), nor was it able to recognize these tunes."
Date
Source https://arxiv.org/abs/2303.12712
Author Authors of the study: Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang (all at Microsoft Research)

Licensing edit

w:en:Creative Commons
attribution
This file is licensed under the Creative Commons Attribution 4.0 International license.
You are free:
  • to share – to copy, distribute and transmit the work
  • to remix – to adapt the work
Under the following conditions:
  • attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

File history

Click on a date/time to view the file as it appeared at that time.

Date/TimeThumbnailDimensionsUserComment
current14:11, 8 May 2023Thumbnail for version as of 14:11, 8 May 20231,304 × 1,909 (450 KB)Prototyperspective (talk | contribs)Uploaded a work by Authors of the study: Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang (all at Microsoft Research) from https://arxiv.org/abs/2303.12712 with UploadWizard

There are no pages that use this file.