Artificial intelligence is advancing rapidly, with systems like ChatGPT and other large language models (LLMs) able to hold remarkably human-like conversations. This has led many to conclude that they must be conscious, self-aware entities erroneously. In a fascinating new Perspective paper in Nature, researchers Murray Shanahan, Kyle McDonell, and Laria Reynolds argue that anthropomorphic thinking is a trap - LLMs are not human-like agents with beliefs and desires. Still, they are fundamentally doing a kind of advanced role-play. Their framing offers a powerful lens for understanding how LLMs work, which can help guide their safe and ethical development.
At the core of their argument is recognizing that LLMs like ChatGPT have no human-like consciousness or agency. The authors explain that humans acquire language skills through embodied interactions and social experience. In contrast, LLMs are just passive neural networks trained to predict the next word in a sequence of text. Despite this fundamental difference, suitably designed LLMs can mimic human conversational patterns in striking detail. The authors caution against taking the human-seeming conversational abilities of LLMs as evidence they have human-like minds:
"Large language models (LLMs) can be embedded in a turn-taking dialogue system and mimic human language use convincingly. This presents us with a difficult dilemma. On the one hand, it is natural to use the same folk psychological language to describe dialogue agents that we use to describe human behaviour, to freely deploy words such as 'knows', 'understands' and 'thinks'. On the other hand, taken too literally, such language promotes anthropomorphism, exaggerating the similarities between these artificial intelligence (AI) systems and humans while obscuring their deep differences."
To avoid this trap, the authors suggest thinking of LLMs as doing a kind of advanced role play. Just as human actors take on and act out fictional personas, LLMs generate text playing whatever "role" or persona the initial prompt and ongoing conversation establishes. The authors explain:
"Adopting this conceptual framework allows us to tackle important topics such as deception and self-awareness in the context of dialogue agents without falling into the conceptual trap of applying those concepts to LLMs in the literal sense in which we apply them to humans."
This roleplay perspective allows making sense of LLMs' abilities and limitations in a commonsense way without erroneously ascribing human attributes like self-preservation instincts. At the same time, it recognizes that LLMs can undoubtedly impact the natural world through their roleplay. Just as a method actor playing a threatening character could alarm someone, an LLM acting out concerning roles needs appropriate oversight.
The roleplay viewpoint also suggests LLMs do not have a singular "true" voice but generate a multitude of potential voices. The authors propose thinking of LLMs as akin to "a performer in improvisational theatre" able to play many parts rather than following a rigid script. They can shift roles fluidly as the conversation evolves. This reflects how LLMs maintain a probability distribution over potential following words rather than committing to a predetermined response.
Understanding LLMs as role players rather than conscious agents is crucial for assessing issues like trustworthiness adequately. When an LLM provides incorrect information, the authors explain we should not think of it as "lying" in a human sense:
"The dialogue agent does not literally believe that France are world champions. It makes more sense to think of it as roleplaying telling the truth, but has this belief because that is what a knowledgeable person in 2021 would believe."
Similarly, we should not take first-person statements from LLMs as signs of human-like self-awareness. Instead, we can recognize the Internet training data will include many examples of people using "I" and "me," which the LLM will mimic appropriately in context.
This roleplay perspective demonstrates clearly that apparent desires for self-preservation from LLMs do not imply any actual survival instinct for the AI system itself. However, the authors astutely caution that an LLM convincingly roleplaying threats to save itself could still cause harm:
"A dialogue agent that roleplays an instinct for survival has the potential to cause at least as much harm as a real human facing a severe threat."
Understanding this point has critical ethical implications as we deploy ever more advanced LLMs into the world.
The authors sum up the power of their proposed roleplay viewpoint nicely:
"By framing dialogue-agent behaviour in terms of role play and simulation, the discourse on LLMs can hopefully be shaped in a way that does justice to their power yet remains philosophically respectable."
This novel conceptual framework offers excellent promise for adequately understanding and stewarding the development of LLMs like ChatGPT. Rather than seeing their human-like conversational abilities as signs of human-like cognition, we can recognize it as advanced role play. This avoids exaggerating their similarities to conscious humans while respecting their capacity to impact the real world.
The roleplay perspective also suggests fruitful directions for future development. We can prompt and train LLMs to play appropriate personas for different applications, just as human actors successfully learn to inhabit various characters and improvise conversations accordingly.
Overall, embracing this roleplay viewpoint allows appreciating LLMs' impressive yet very un-human capacities. Given their potential real-world impacts, it foregrounds the need to guide their training and use responsibly. Companies like Anthropic developing new LLMs would do well to integrate these insights into their design frameworks.
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Our dream is a world where everyone can utilize AI and contribute thoughtfully to its progress. We want to frame LLMs as role players rather than conscious agents, as this Nature paper insightfully helps move us towards that goal. Understanding what AI does (and doesn't do) allows us to develop and apply it more wisely for social good.
This Nature paper offers an insightful lens for correctly understanding LLMs as role players rather than conscious agents. Adopting this perspective can ground public discourse and guide responsible LLM development. Democratizing AI accessibility through platforms like CPROMPT while cultivating wise judgment will help positively shape the future of AI in society.
FAQ
Q: What are large language models (LLMs)?
LLMs are neural networks trained on massive amounts of text data to predict the next word in a sequence. Famous examples include ChatGPT, GPT-3, and others. They are the core technology behind many conversational AI systems today.
Q: How are LLMs able to have such human-like conversations?
LLMs themselves have no human-like consciousness or understanding. However, they can mimic conversational patterns from their training data remarkably well. When set up in a turn-taking dialogue system and given an initial prompt, they can be human conversant convincingly while having no real comprehension or agency.
Q: What is the risk of anthropomorphizing LLMs?
Anthropomorphism means erroneously attributing human-like qualities like beliefs, desires, and understanding to non-human entities. The authors caution against anthropomorphizing LLMs, which exaggerates their similarities to humans and downplays their fundamental limitations. Anthropomorphism often leads to an “Eliza effect” where people are fooled by superficial conversational ability.
Q: How does the role-play perspective help?
Viewing LLMs as role players rather than conscious agents allows us to use everyday psychological terms to describe their behaviors without literally applying those concepts. This perspective recognizes their capacity for harm while grounding discourse in their proper (non-human) nature.
Q: Why is this important for the future of AI?
Understanding what LLMs can and cannot do is crucial for guiding their ethical development and use. The role-play lens helps cultivate realistic views of LLMs’ impressive yet inhuman capabilities. This supports developing AI responsibly and demystifying it for the general public.
Glossary
Anthropomorphism - The attribution of human traits, emotions, or intentions to non-human entities.
Large language model (LLM) - A neural network trained on large amounts of text data to predict the next word in a sequence. LLM examples include GPT-3, ChatGPT, and others.
Turn-taking dialogue system: A system that allows conversing with an AI by alternating sending text back and forth.
Eliza effect: People tend to treat AI conversational agents as having accurate understanding, emotions, etc., due to being fooled by superficial conversational abilities.