@inproceedings{alexeeva-etal-2022-combining,
title = "Combining Extraction and Generation for Constructing Belief-Consequence Causal Links",
author = "Alexeeva, Maria and
Beal Cohen, Allegra A. and
Surdeanu, Mihai",
editor = "Tafreshi, Shabnam and
Sedoc, Jo{\~a}o and
Rogers, Anna and
Drozd, Aleksandr and
Rumshisky, Anna and
Akula, Arjun",
booktitle = "Proceedings of the Third Workshop on Insights from Negative Results in NLP",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.insights-1.22",
doi = "10.18653/v1/2022.insights-1.22",
pages = "159--164",
abstract = "In this paper, we introduce and justify a new task{---}causal link extraction based on beliefs{---}and do a qualitative analysis of the ability of a large language model{---}InstructGPT-3{---}to generate implicit consequences of beliefs. With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task.",
}
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<abstract>In this paper, we introduce and justify a new task—causal link extraction based on beliefs—and do a qualitative analysis of the ability of a large language model—InstructGPT-3—to generate implicit consequences of beliefs. With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task.</abstract>
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%0 Conference Proceedings
%T Combining Extraction and Generation for Constructing Belief-Consequence Causal Links
%A Alexeeva, Maria
%A Beal Cohen, Allegra A.
%A Surdeanu, Mihai
%Y Tafreshi, Shabnam
%Y Sedoc, João
%Y Rogers, Anna
%Y Drozd, Aleksandr
%Y Rumshisky, Anna
%Y Akula, Arjun
%S Proceedings of the Third Workshop on Insights from Negative Results in NLP
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F alexeeva-etal-2022-combining
%X In this paper, we introduce and justify a new task—causal link extraction based on beliefs—and do a qualitative analysis of the ability of a large language model—InstructGPT-3—to generate implicit consequences of beliefs. With the language model-generated consequences being promising, but not consistent, we propose directions of future work, including data collection, explicit consequence extraction using rule-based and language modeling-based approaches, and using explicitly stated consequences of beliefs to fine-tune or prompt the language model to produce outputs suitable for the task.
%R 10.18653/v1/2022.insights-1.22
%U https://aclanthology.org/2022.insights-1.22
%U https://doi.org/10.18653/v1/2022.insights-1.22
%P 159-164
Markdown (Informal)
[Combining Extraction and Generation for Constructing Belief-Consequence Causal Links](https://aclanthology.org/2022.insights-1.22) (Alexeeva et al., insights 2022)
ACL