Causality for Natural Language Processing

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URI: http://hdl.handle.net/10900/159746
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1597469
http://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1597465
http://nbn-resolving.org/urn:nbn:de:bsz:21-dspace-1597467
http://dx.doi.org/10.15496/publikation-101079
Dokumentart: PhDThesis
Date: 2024-12-18
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Informatik
Advisor: Schölkopf, Bernhard (Prof. Dr.)
Day of Oral Examination: 2024-12-13
DDC Classifikation: 004 - Data processing and computer science
Keywords: Computerlinguistik , Sprachdaten , Maschinelles Lernen , Künstliche Intelligenz
Other Keywords: natürlichen Sprachverarbeitung
Kausales Denken
Maschinelles Lernen
Künstliche Intelligenz
großen Sprachmodellen
large language models
artificial intelligence
machine learning
causal reasoning
Natural language processing
License: http://tobias-lib.uni-tuebingen.de/doku/lic_ohne_pod.php?la=de http://tobias-lib.uni-tuebingen.de/doku/lic_ohne_pod.php?la=en
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Abstract:

Causal reasoning is a cornerstone of human intelligence and a critical capability for artificial systems aiming to achieve advanced understanding and decision-making. This thesis delves into various dimensions of causal reasoning and understanding in large language models (LLMs). It encompasses a series of studies that explore the causal inference skills of LLMs, the mechanisms behind their performance, and the implications of causal and anticausal learning for natural language processing (NLP) tasks. Additionally, it investigates the application of causal reasoning in text-based computational social science, specifically focusing on political decision-making and the evaluation of scientific impact through citations. Through novel datasets, benchmark tasks, and methodological frameworks, this work identifies key challenges and opportunities to improve the causal capabilities of LLMs, providing a comprehensive foundation for future research in this evolving field.

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