Towards an Analysis of Discourse and Interactional Pragmatic Reasoning Capabilities of Large Language Models
- URL: http://arxiv.org/abs/2408.03074v1
- Date: Tue, 6 Aug 2024 10:02:05 GMT
- Title: Towards an Analysis of Discourse and Interactional Pragmatic Reasoning Capabilities of Large Language Models
- Authors: Amelie Robrecht, Judith Sieker, Clara Lachenmaier, Sina Zarieß, Stefan Kopp,
- Abstract summary: We discuss the scope of the field of pragmatics and suggest a subdivision into discourse pragmatics and interactional pragmatics.
We consider the resulting heterogeneous set of phenomena and methods as a starting point for our survey of work on discourse pragmatics and interactional pragmatics in the context of LLMs.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In this work, we want to give an overview on which pragmatic abilities have been tested in LLMs so far and how these tests have been carried out. To do this, we first discuss the scope of the field of pragmatics and suggest a subdivision into discourse pragmatics and interactional pragmatics. We give a non-exhaustive overview of the phenomena of those two subdomains and the methods traditionally used to analyze them. We subsequently consider the resulting heterogeneous set of phenomena and methods as a starting point for our survey of work on discourse pragmatics and interactional pragmatics in the context of LLMs.
Related papers
- The Pragmatic Mind of Machines: Tracing the Emergence of Pragmatic Competence in Large Language Models [6.187227278086245]
Large language models (LLMs) have demonstrated emerging capabilities in social intelligence, including implicature resolution and theory-of-mind reasoning.<n>In this work, we evaluate whether LLMs at different training stages can accurately infer speaker intentions.<n>We systematically evaluate 22 LLMs across 3 key training stages: after pre-training, supervised fine-tuning (SFT), and preference optimization.
arXiv Detail & Related papers (2025-05-24T04:24:59Z) - Talking Point based Ideological Discourse Analysis in News Events [62.18747509565779]
We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.
Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion.
We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
arXiv Detail & Related papers (2025-04-10T02:52:34Z) - Why Reasoning Matters? A Survey of Advancements in Multimodal Reasoning (v1) [66.51642638034822]
Reasoning is central to human intelligence, enabling structured problem-solving across diverse tasks.
Recent advances in large language models (LLMs) have greatly enhanced their reasoning abilities in arithmetic, commonsense, and symbolic domains.
This paper offers a concise yet insightful overview of reasoning techniques in both textual and multimodal LLMs.
arXiv Detail & Related papers (2025-04-04T04:04:56Z) - Diagnosing Moral Reasoning Acquisition in Language Models: Pragmatics and Generalization [9.650922370722476]
Large Language Models (LLMs) often fail to perform satisfactorily on tasks requiring moral cognizance.
Can current learning paradigms enable LLMs to acquire sufficient moral reasoning capabilities?
We show that performance improvements follow a mechanism similar to that of semantic-level tasks, and therefore remain affected by the pragmatic nature of latent morals in discourse.
arXiv Detail & Related papers (2025-02-23T15:00:53Z) - LogiDynamics: Unraveling the Dynamics of Logical Inference in Large Language Model Reasoning [49.58786377307728]
This paper adopts an exploratory approach by introducing a controlled evaluation environment for analogical reasoning.
We analyze the comparative dynamics of inductive, abductive, and deductive inference pipelines.
We investigate advanced paradigms such as hypothesis selection, verification, and refinement, revealing their potential to scale up logical inference.
arXiv Detail & Related papers (2025-02-16T15:54:53Z) - Do Large Language Models Advocate for Inferentialism? [0.0]
The emergence of large language models (LLMs) such as ChatGPT and Claude presents new challenges for philosophy of language.<n>This paper explores Robert Brandom's inferential semantics as an alternative foundational framework for understanding these systems.
arXiv Detail & Related papers (2024-12-19T03:48:40Z) - Categorical Syllogisms Revisited: A Review of the Logical Reasoning Abilities of LLMs for Analyzing Categorical Syllogism [62.571419297164645]
This paper provides a systematic overview of prior works on the logical reasoning ability of large language models for analyzing categorical syllogisms.
We first investigate all the possible variations for the categorical syllogisms from a purely logical perspective.
We then examine the underlying configurations (i.e., mood and figure) tested by the existing datasets.
arXiv Detail & Related papers (2024-06-26T21:17:20Z) - Understanding In-Context Learning with a Pelican Soup Framework [27.144616560712493]
We propose a theoretical framework to explain in-context learning for natural language processing.
Our results demonstrate the efficacy of our framework to explain in-context learning.
arXiv Detail & Related papers (2024-02-16T03:20:14Z) - An Encoding of Abstract Dialectical Frameworks into Higher-Order Logic [57.24311218570012]
This approach allows for the computer-assisted analysis of abstract dialectical frameworks.
Exemplary applications include the formal analysis and verification of meta-theoretical properties.
arXiv Detail & Related papers (2023-12-08T09:32:26Z) - Regularized Conventions: Equilibrium Computation as a Model of Pragmatic
Reasoning [72.21876989058858]
We present a model of pragmatic language understanding, where utterances are produced and understood by searching for regularized equilibria of signaling games.
In this model speakers and listeners search for contextually appropriate utterance--meaning mappings that are both close to game-theoretically optimal conventions and close to a shared, ''default'' semantics.
arXiv Detail & Related papers (2023-11-16T09:42:36Z) - How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored.
Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges.
We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
arXiv Detail & Related papers (2023-11-14T08:51:00Z) - The Empty Signifier Problem: Towards Clearer Paradigms for
Operationalising "Alignment" in Large Language Models [18.16062736448993]
We address the concept of "alignment" in large language models (LLMs) through the lens of post-structuralist socio-political theory.
We propose a framework that demarcates: 1) which dimensions of model behaviour are considered important, then 2) how meanings and definitions are ascribed to these dimensions.
We aim to foster a culture of transparency and critical evaluation, aiding the community in navigating the complexities of aligning LLMs with human populations.
arXiv Detail & Related papers (2023-10-03T22:02:17Z) - DiPlomat: A Dialogue Dataset for Situated Pragmatic Reasoning [89.92601337474954]
Pragmatic reasoning plays a pivotal role in deciphering implicit meanings that frequently arise in real-life conversations.
We introduce a novel challenge, DiPlomat, aiming at benchmarking machines' capabilities on pragmatic reasoning and situated conversational understanding.
arXiv Detail & Related papers (2023-06-15T10:41:23Z) - A practical introduction to the Rational Speech Act modeling framework [2.1485350418225244]
Recent advances in computational cognitive science paved the way for significant progress in formal, implementable models of pragmatics.
This paper provides a practical introduction to and critical assessment of the Bayesian Rational Speech Act modeling framework.
arXiv Detail & Related papers (2021-05-20T16:08:04Z) - How Far are We from Effective Context Modeling? An Exploratory Study on
Semantic Parsing in Context [59.13515950353125]
We present a grammar-based decoding semantic parsing and adapt typical context modeling methods on top of it.
We evaluate 13 context modeling methods on two large cross-domain datasets, and our best model achieves state-of-the-art performances.
arXiv Detail & Related papers (2020-02-03T11:28:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.