Can humans teach machines to code?
Fuente:
arXiv
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915153371463680 |
|---|---|
| author | Hocquette, Céline Langer, Johannes Cropper, Andrew Schmid, Ute |
| author_facet | Hocquette, Céline Langer, Johannes Cropper, Andrew Schmid, Ute |
| contents | The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples. A key underlying assumption is that humans can provide sufficient examples to teach a concept to a machine. To evaluate the validity of this assumption, we conduct a study where human participants provide examples for six programming concepts, such as finding the maximum element of a list. We evaluate the generalisation performance of five program synthesis systems trained on input-output examples (i) from non-expert humans, (ii) from a human expert, and (iii) randomly sampled. Our results suggest that non-experts typically do not provide sufficient examples for a program synthesis system to learn an accurate program. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_19397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Can humans teach machines to code? Hocquette, Céline Langer, Johannes Cropper, Andrew Schmid, Ute Human-Computer Interaction Machine Learning The goal of inductive program synthesis is for a machine to automatically generate a program from user-supplied examples. A key underlying assumption is that humans can provide sufficient examples to teach a concept to a machine. To evaluate the validity of this assumption, we conduct a study where human participants provide examples for six programming concepts, such as finding the maximum element of a list. We evaluate the generalisation performance of five program synthesis systems trained on input-output examples (i) from non-expert humans, (ii) from a human expert, and (iii) randomly sampled. Our results suggest that non-experts typically do not provide sufficient examples for a program synthesis system to learn an accurate program. |
| title | Can humans teach machines to code? |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2404.19397 |