"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
Informations
- Type:
- inproceedings
- Auteurs:
- Michael Xieyang Liu and Advait Sarkar and Carina Negreanu and Benjamin Zorn and Jack Williams and Ne...
- Pertinence:
-
Moyenne
- Référence:
- Liu_2023
- Doi:
- 10.1145/3544548.3580817
- Mots-clés:
- Url:
- https://doi.org/10.1145%2F3544548.3580817
- Date de publication:
- 04/2023
- Résumé:
- natural langage programming (+ interpretation du code python génété) pour des utilisateurs qui ne savent pas programmer. Interface pour décrire plusieurs étapes et résultat attendu (debugging?)
- Abstract:
- Code-generating large language models translate natural language into code. However, only a small portion of the infinite space of naturalistic utterances is effective at guiding code generation. For non-expert end-user programmers, learning this is the challenge of abstraction matching. We examine this challenge in the specific context of data analysis in spreadsheets, in a system that maps the users natural language query to Python code using the Codex generator, executes the code, and shows the result. We propose grounded abstraction matching, which bridges the abstraction gap by translating the code back into a systematic and predictable naturalistic utterance. In a between-subjects, think-aloud study (n=24), we compare grounded abstraction matching to an ungrounded alternative based on previously established query framing principles. We find that the grounded approach improves end-users' understanding of the scope and capabilities of the code-generating model, and the kind of language needed to use it effectively.
- Pdf:
- Lien pdf
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Auteurs
1 auteurs
| Nom | Nombre d'articles | Actions |
|---|---|---|
| Michael Xieyang Liu and Advait Sarkar and Carina Negreanu and Benjamin Zorn and Jack Williams and Neil Toronto and Andrew D. Gordon | 1 |