We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.
Feedback

Pragmatic processing in large language models

Formale Metadaten

Titel
Pragmatic processing in large language models
Serientitel
Anzahl der Teile
17
Autor
Lizenz
CC-Namensnennung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen.
Identifikatoren
Herausgeber
Erscheinungsjahr
Sprache

Inhaltliche Metadaten

Fachgebiet
Genre
Abstract
In linguistics, pragmatics refers to meaning that is intended but not spelled out literally, i.e. it concerns what we humans understand beyond the semantic meaning of a sentence or text. Prominent examples of these include for instance scalar implicatures, such as the utterance “Some students failed", which is in its literal meaning compatible with a situation where all students failed, but which is usually interpreted as “some but not all" students having failed. Traditionally, these phenomena are modelled using game-theoretic models of human interaction (such as the rational speech act (RSA) model, Frank and Goodman, 2012). In my talk, I will provide an overview of how well recent large language models like ChatGPT are performing on pragmatic tasks - largely showing that they fail in many respects, and will then proceed to analyse what may be lacking from current LLMs.