Talking about Large Language Models

By Murray Shanahan. Communications of the ACM, 2022

479 citations recorded by the source corpus.

Interacting with a contemporary LLM-based conversational agent can create an illusion of being in the presence of a thinking creature. Yet, in their very nature, such systems are fundamentally not like us.

Subfields: Artificial Intelligence, Cultural Studies

View Paper: https://www.semanticscholar.org/paper/3eed4de25636ac90f39f6e1ef70e3507ed61a2a6

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Citations in the corpus

Detecting hallucinations in large language models using semantic entropy (2024, unknown): “Although we agree that metaphor must be used carefully with LLMs 22 , the widespread adoption of the term hallucination reflects the fact that it points to an important phenomenon.”
A Survey of Large Language Models (2023, unknown): “Typically, large language models (LLMs) refer to language models that contain hundreds of billions (or more) of parameters4, which are trained on massive text data [32], such as GPT-3 [55], PaLM [56], Galactica [35], and LLaMA [57].”
Mirages: On Anthropomorphism in Dialogue Systems (2023, background): “Terminology There is increasing awareness that the anthropomorphic language and jargon used to describe technologies such as language models contributes to inaccurate perceptions of their capabilities, particularly among the general public (Hunger, 2023; Salles et al., 2020; Shanahan, 2023).”
Role play with large language models (2023, background): “On the other hand, taken too literally, such language promotes anthropomorphism, exaggerating the similarities between these AI systems and humans while obscuring their deep differences (Shanahan, 2023).”
Large Language Models for Software Engineering: A Systematic Literature Review (2023, background): “Researchers have observed that scaling up the model sizes significantly enhances their capacity, leading to remarkable performance improvements when the parameter scale surpasses a certain threshold [95, 246, 260].”
Understanding Natural Language Understanding Systems. A Critical Analysis (2023, background): “This is an extreme case, but media worldwide are full of articles that hail these systems and describe their «extraordinary» abilities in strongly humanized terms (Shanahan 2022).”
When large language models meet personalization: perspectives of challenges and opportunities (2023, unknown): “Secondly, the language models are pre-trained at scale with a massive amount of unlabeled corpus [3, 30–32].”
Software Testing With Large Language Models: Survey, Landscape, and Vision (2023, unknown): “These models, initially pre-trained on extensive corpora, have exhibited remarkable performance across a wide range of NLP tasks including question-answering, machine translation, and text generation [16], [17], [18], [19].”
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning (2023, background): “While chain-of-thought may imitate human reasoning processes, the fundamental nature of LLMs remains that of black-box probabilistic models, lacking a mechanism to guarantee the faithfulness of reasoning (Shanahan, 2022).”
Measuring the Feasibility of a Question and Answering System for the Sarawak Gazette Using Chatbot Technology (2025, unknown): “These massive PLMs have been referred to as Large Language Models (LLMs) (Shanahan, 2024), which have attracted significant research attention due to their emergent behaviors (Zhao et al, 2023).”