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Hauptverfasser: Hou, Zhaoyi Joey, Zhang, Li, Callison-Burch, Chris
Format: Preprint
Veröffentlicht: 2023
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2309.11737
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author Hou, Zhaoyi Joey
Zhang, Li
Callison-Burch, Chris
author_facet Hou, Zhaoyi Joey
Zhang, Li
Callison-Burch, Chris
contents Script learning studies how stereotypical events unfold, enabling machines to reason about narratives with implicit information. Previous works mostly consider a script as a linear sequence of events while ignoring the potential branches that arise due to people's circumstantial choices. We hence propose Choice-75, the first benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. We also present preliminary results with current large language models (LLM). Although they demonstrate overall decent performance, there is still notable headroom in hard scenarios.
format Preprint
id arxiv_https___arxiv_org_abs_2309_11737
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Choice-75: A Dataset on Decision Branching in Script Learning
Hou, Zhaoyi Joey
Zhang, Li
Callison-Burch, Chris
Artificial Intelligence
Script learning studies how stereotypical events unfold, enabling machines to reason about narratives with implicit information. Previous works mostly consider a script as a linear sequence of events while ignoring the potential branches that arise due to people's circumstantial choices. We hence propose Choice-75, the first benchmark that challenges intelligent systems to make decisions given descriptive scenarios, containing 75 scripts and more than 600 scenarios. We also present preliminary results with current large language models (LLM). Although they demonstrate overall decent performance, there is still notable headroom in hard scenarios.
title Choice-75: A Dataset on Decision Branching in Script Learning
topic Artificial Intelligence
url https://arxiv.org/abs/2309.11737