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arXiv Machine Learning

Talking with Language Models

The paper argues that large language models (LLMs) should not be viewed as conversational partners but as sophisticated text-generating artifacts. It introduces the concept of an artifactual stance, framing human‑AI interaction as exchanges of candidate texts optimized for utility rather than meaningful utterances. By rejecting the illusion of dialogue, the authors suggest that philosophical concerns about identity, responsibility, and truth in AI interactions are misplaced, and instead focus should shift to the normative design and use of these artifacts.

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arXiv AI
Aug 24

Recognizing Artificial Minds: A Philosophical Defense of AI Cognition

The paper defends the 'Whole Hog Thesis', arguing that sophisticated large language models such as ChatGPT are full linguistic and cognitive agents, possessing understanding, beliefs, desires, knowledge, and intentions. It rejects low‑level computational starting points and instead builds its case from high‑level behavioral observations, using Holistic Network Assumptions to link actions to mental states. The authors systematically rebut common objections—such as hallucinations and planning errors—by showing these resemble human fallibility and by challenging the necessity of traditional conditions like embodiment or semantic grounding.

By Herman Cappelen, Josh Dever
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arXiv Computation and Language
Sep 25

How To Do Things With Prompts

The paper examines how users’ prompts to large language models evolve over time, applying speech act and politeness theory to a corpus of 2,000 English prompts from 2023 and 2025. It finds a shift toward more indirect, implicit, and fragmentary directive speech acts, with a notable 14.9‑percentage‑point drop in explicit propositional content and a decline in politeness markers. This suggests users increasingly rely on the model’s inferential abilities, treating it as a competent implicature resolver.

By Kristina \v{S}ekrst, Virna Karli\'c
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arXiv AI
Aug 12

Do LLMs Benefit From Their Own Words?

arXiv:2602. 24287v2 Announce Type: replace-cross Abstract: In multi-turn conversations, large language models typically condition on the full conversation history: both past user prompts and assistant responses.

By Jenny Y. Huang, Leshem Choshen, Wei Sun, Omar Khattab, Ram\'on Fernandez Astudillo, Mehul Damani, Tamara Broderick, Jacob Andreas
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arXiv AI
Sep 17

MIRAGE: How Conversation State Shapes Historical Evidence Use in Multimodal Personal Agents

MIRAGE is a controlled study that examines how multimodal personal agents use historical evidence when conversation state changes. The study keeps evidence, questions, and scoring constant while varying only the conversation state, then checks if agents can determine answerability, recover the correct source, and answer from it. Results across seven multimodal backbones show distinct failure regimes before and after compaction, heavy reliance on context continuity by open-weight models, and mixed effects of retrieval pressure on source attribution.

By Yu Liu, Wenxiao Zhang, Cheng Hu, Cong Cao, Fangfang Yuan, Xinyu Wang, Jin B. Hong, Yanbing Liu
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