Existential Conversations with Large Language Models: Content, Community, and Culture

• URL: http://arxiv.org/abs/2411.13223v1
• Date: Wed, 20 Nov 2024 11:35:22 GMT
• Title: Existential Conversations with Large Language Models: Content, Community, and Culture
• Authors: Murray Shanahan, Beth Singler,
• Abstract summary: Large language models can engage users on a wide variety of topics, including philosophy, spirituality, and religion. We trace likely sources, both ancient and modern, for the extensive repertoire of images, myths, metaphors, and conceptual esoterica that the language model draws on. We consider the larger societal impacts of such engagements with LLMs.
• Score: 11.573256071600722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Contemporary conversational AI systems based on large language models (LLMs) can engage users on a wide variety of topics, including philosophy, spirituality, and religion. Suitably prompted, LLMs can be coaxed into discussing such existentially significant matters as their own putative consciousness and the role of artificial intelligence in the fate of the Cosmos. Here we examine two lengthy conversations of this type. We trace likely sources, both ancient and modern, for the extensive repertoire of images, myths, metaphors, and conceptual esoterica that the language model draws on during these conversations, and foreground the contemporary communities and cultural movements that deploy related motifs, especially in their online activity. Finally, we consider the larger societal impacts of such engagements with LLMs.

Related papers

• ConlangCrafter: Constructing Languages with a Multi-Hop LLM Pipeline [33.79479733542137]
  We leverage modern LLMs as computational creativity aids for end-to-end conlang creation.<n>We introduce ConlangCrafter, a multi-hop pipeline that decomposes language design into modular stages.<n>We evaluate ConlangCrafter on metrics measuring consistency and typological diversity.
  arXiv  Detail & Related papers  (2025-08-08T07:36:48Z)
• On the Semantics of Large Language Models [0.0]
  Large Language Models (LLMs) demonstrated the potential to replicate human language abilities through technology.<n>It remains controversial to what extent these systems truly understand language.<n>We examine this issue by narrowing the question down to the semantics of LLMs at the word and sentence level.
  arXiv  Detail & Related papers  (2025-07-07T20:02:57Z)
• Seamless Interaction: Dyadic Audiovisual Motion Modeling and Large-Scale Dataset [113.25650486482762]
  We introduce the Seamless Interaction dataset, a large-scale collection of over 4,000 hours of face-to-face interaction footage.<n>This dataset enables the development of AI technologies that understand dyadic embodied dynamics.<n>We develop a suite of models that utilize the dataset to generate dyadic motion gestures and facial expressions aligned with human speech.
  arXiv  Detail & Related papers  (2025-06-27T18:09:49Z)
• Large Language Models Understanding: an Inherent Ambiguity Barrier [0.0]
  A lively ongoing debate is taking place, since the emergence of Large Language Models (LLMs) with regards to their capability to understand the world. Arguments and counter-arguments have been proposed based upon thought experiments, anecdotal conversations between LLMs and humans, statistical linguistic analysis, philosophical considerations, and more. In this brief paper we present a counter-argument based upon a thought experiment and semi-formal considerations leading to an inherent ambiguity barrier.
  arXiv  Detail & Related papers  (2025-05-01T16:55:44Z)
• LLMs syntactically adapt their language use to their conversational partner [58.92470092706263]
  It has been frequently observed that human speakers align their language use with each other during conversations. We construct a corpus of conversations between large language models (LLMs) and find that two LLM agents end up making more similar syntactic choices as conversations go on.
  arXiv  Detail & Related papers  (2025-03-10T15:37:07Z)
• Taking Notes Brings Focus? Towards Multi-Turn Multimodal Dialogue Learning [32.95008932216176]
  We introduce MMDiag, a multi-turn multimodal dialogue dataset. We also present DiagNote, an MLLM equipped with multimodal grounding and reasoning capabilities.
  arXiv  Detail & Related papers  (2025-03-10T07:32:53Z)
• Towards Anthropomorphic Conversational AI Part I: A Practical Framework [49.62013440962072]
  We introduce a multi- module framework designed to replicate the key aspects of human intelligence involved in conversations. In the second stage of our approach, these conversational data, after filtering and labeling, can serve as training and testing data for reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-28T03:18:39Z)
• Multi-ToM: Evaluating Multilingual Theory of Mind Capabilities in Large Language Models [3.9532244541907793]
  Theory of Mind (ToM) refers to the cognitive ability to infer and attribute mental states to oneself and others. It remains unclear to what extent large language models (LLMs) demonstrate ToM across diverse languages and cultural contexts. This paper introduces a comprehensive study of multilingual ToM capabilities aimed at addressing this gap.
  arXiv  Detail & Related papers  (2024-11-24T22:37:59Z)
• Large Language Models Reflect the Ideology of their Creators [73.25935570218375]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. We uncover notable diversity in the ideological stance exhibited across different LLMs and languages.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models [16.583370726582356]
  We show that Vision Language Models (VLMs) can implicitly understand sound-based phenomena via abstract reasoning from orthography and imagery alone. We perform experiments including replicating the classic Kiki-Bouba and Mil-Mal shape and magnitude symbolism tasks. Our results show that VLMs demonstrate varying levels of agreement with human labels, and more task information may be required for VLMs versus their human counterparts for in silico experimentation.
  arXiv  Detail & Related papers  (2024-09-23T11:13:25Z)
• Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile Instructions [68.98811048970963]
  We present a pioneering effort to investigate the capability of large language models (LLMs) in transcribing speech in multi-talker environments. Our approach utilizes WavLM and Whisper encoder to extract multi-faceted speech representations that are sensitive to speaker characteristics and semantic context. Comprehensive experiments reveal the promising performance of our proposed system, MT-LLM, in cocktail party scenarios.
  arXiv  Detail & Related papers  (2024-09-13T07:28:28Z)
• Large Language Models can impersonate politicians and other public figures [47.2573979612036]
  Modern AI technology like Large language models (LLMs) has the potential to pollute the public information sphere with made-up content. We present the results of a study based on a cross-section of British society. LLMs are able to generate responses to debate questions that were part of a broadcast political debate programme in the UK.
  arXiv  Detail & Related papers  (2024-07-09T11:16:19Z)
• SoMeLVLM: A Large Vision Language Model for Social Media Processing [78.47310657638567]
  We introduce a Large Vision Language Model for Social Media Processing (SoMeLVLM) SoMeLVLM is a cognitive framework equipped with five key capabilities including knowledge & comprehension, application, analysis, evaluation, and creation. Our experiments demonstrate that SoMeLVLM achieves state-of-the-art performance in multiple social media tasks.
  arXiv  Detail & Related papers  (2024-02-20T14:02:45Z)
• A Philosophical Introduction to Language Models -- Part I: Continuity With Classic Debates [0.05657375260432172]
  This article serves both as a primer on language models for philosophers, and as an opinionated survey of their significance. We argue that the success of language models challenges several long-held assumptions about artificial neural networks. This sets the stage for the companion paper (Part II), which turns to novel empirical methods for probing the inner workings of language models.
  arXiv  Detail & Related papers  (2024-01-08T14:12:31Z)
• Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue [73.69510478736483]
  Large language models (LLMs) can generate fluent, coherent, and diverse responses. However, they lack a crucial ability: communication skills. This article aims to empower LLMs with communication skills through inner monologues. Experimental results show that the proposed CSIM strategy improves the backbone models and outperforms the baselines.
  arXiv  Detail & Related papers  (2023-11-13T16:19:42Z)
• The Quo Vadis of the Relationship between Language and Large Language Models [3.10770247120758]
  Large Language Models (LLMs) have come to encourage the adoption of LLMs as scientific models of language. We identify the most important theoretical and empirical risks brought about by the adoption of scientific models that lack transparency. We conclude that, at their current stage of development, LLMs hardly offer any explanations for language.
  arXiv  Detail & Related papers  (2023-10-17T10:54:24Z)
• Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
  We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
  arXiv  Detail & Related papers  (2023-10-10T03:06:38Z)

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.