2023 is the year of LLMs. ChatGPT, GPT-4, LAMA and, more. One new LLM model is attracting attention after another. These models have revolutionized the field of natural language processing and are increasingly used in various domains.
LLMs have a remarkable ability to exhibit a variety of behaviors, including engaging in conversation, leading to a compelling illusion of conversing with a human-like interlocutor. However, it is important to recognize that LLM-based conversational agents differ significantly from humans in many respects.
Our language skills develop through interactions with the world. As individuals, we acquire cognitive skills and linguistic abilities through socialization and immersion in a community of language users. This part happens faster in children, and as we age, our learning process slows down; But the basics remain the same.
In contrast, LLMs are neural networks trained on a wide range of human-generated text, whose primary objective is to predict the next word or token based on a given context. Their training revolves around learning statistical patterns from linguistic data rather than through direct experience of the physical world.
Despite these differences, we use LLMs to mimic humans. We do this with chatbots, assistants, etc. However, this approach poses a challenging dilemma. How to describe and understand the behavior of LLMs?
It is natural to use familiar folk-psychological language to describe conversational agents, like humans. However, when taken too literally, such language promotes anthropomorphism, exaggerating the similarities between AI systems and humans and obscuring their profound differences.
So how do we approach this dilemma? How can we describe the terms „understanding” and „knowing” for AI models? Let’s jump in Role play Paper.
In this paper, the authors propose adopting alternative conceptual frameworks and metaphors to effectively think and talk about LLM-based conversational agents. They argue for two primary metaphors: dialogue sees the agent as acting out a role or as a superposition of simulacra within a variety of characters. These metaphors offer different perspectives in understanding the behavior of conversational agents and have their own distinct advantages.
The first embodiment describes the conversational agent as playing a specific role. Given a prompt, the agent tries to continue the conversation in a way that fits the assigned role or personality. It aims to respond in accordance with the expectations associated with that role.
A second metaphor sees the conversational agent as a collection of different characters from different sources. These agents are trained in a wide variety of materials such as books, scripts, interviews and articles, which gives them a lot of knowledge about a wide variety of characters and storylines. As the conversation continues, the agent adjusts its role and personality based on the training data it has, allowing the role to adapt and respond.
By adopting this framework, researchers and users can explore important aspects of conversational agents, such as deception and self-awareness, without misattributing these concepts to humans. Instead, the focus shifts to understanding how conversational agents behave in role-playing scenarios and the various characters they can impersonate.
In conclusion, LLM-based conversational agents have the ability to simulate human-like conversations, but they differ significantly from real human language users. By viewing conversational agents as role-players or using alternative metaphors, such as combinations of simulations, we can better understand and discuss their behavior. These metaphors provide insights into the complex dynamics of LLM-based conversational systems and help us appreciate their creativity while recognizing their fundamental difference from humans.
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Ekrem Çetinkaya B.Sc. in 2018, and M.Sc. Ozyegin University, Istanbul, Türkiye in 2019. He received his M.Sc. A thesis on image denoising using deep convolutional networks. He received his Ph.D. degree in 2023 from the University of Klagenfurt, Austria, with his dissertation titled „Video Coding Improvements for HTTP Adaptive Streaming Using Machine Learning.” His research interests include deep learning, computer vision, video encoding and multimedia networking.
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