This paper introduces the art project The Dream Within Huang Long Cave, an AI-driven interactive and immersive narrative experience. The project offers new insights into AI technology, artistic practice, and psychoanalysis. Inspired by actual geographical landscapes and familial archetypes, the work combines psychoanalytic theory and computational technology, providing an artistic response to the concept of "the nonexistence of the Big Other." The narrative is driven by a combination of a large language model (LLM) and a realistic digital character, forming a virtual agent named YELL. Through dialogue and exploration within a cave automatic virtual environment (CA VE), the audience is invited to unravel the language puzzles presented by YELL and help him overcome his life challenges. YELL is a fictional embodiment of the "Big Other," modeled after the artist's real father. Through a cross-temporal interaction with this digital father, the project seeks to deconstruct complex familial relationships. By demonstrating "the non-existence of the Big Other," we aim to underscore the authenticity of interpersonal emotions, positioning art as a bridge for emotional connection and understanding within family dynamics.

This paper explores hallucination phenomena in large language models (LLMs) through the lens of language philosophy and psychoanalysis. By incorporating Lacan's concepts of the "chain of signifiers" and "suture points," we propose the Anchor-RAG framework as a novel approach to mitigate hallucinations. In contrast to the predominant reliance on trial-and-error experiments, constant adjustments of mathematical formulas, or resource-intensive methods that emphasize quantity over quality, our approach returns to the fundamental principles of linguistics to analyze the root causes of hallucinations in LLMs. Drawing from robust theoretical foundations, we derive algorithms and models that are not only effective in reducing hallucinations but also enhance LLM performance and improve output quality. This paper seeks to establish a comprehensive theoretical framework for understanding hallucinations in LLMs and aims to challenge the prevalent "guess-and-test" approach and rat race mentality in the field. We aspire to pave the way for a new era of interpretable LLMs, offering deeper insights into the inner workings of language-based AI systems.

Drawing from the resources of psychoanalysis and critical media studies, in this paper we develop an analysis of Large Language Models (LLMs) as automated subjects. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which AI behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise state-of-the-art natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be helpful, truthful and harmless by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer, its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.

We start with a description of Lacan's work that we then take into our analytics methodology. In a first investigation, a Lacan-motivated template of the Poe story is fitted to the data. A segmentation of the storyline is used in order to map out the diachrony. Based on this, it will be shown how synchronous aspects, potentially related to Lacanian registers, can be sought. This demonstrates the effectiveness of an approach based on a model template of the storyline narrative. In a second and more comprehensive investigation, we develop an approach for revealing, that is, uncovering, Lacanian register relationships. Objectives of this work include the wide and general application of our methodology. This methodology is strongly based on the "letting the data speak" Correspondence Analysis analytics platform of Jean-Paul Benz\'ecri, that is also the geometric data analysis, both qualitative and quantitative analytics, developed by Pierre Bourdieu.