Deanthropomorphising NLP: Can a Language Model Be Conscious?
Matthew Shardlow, Piotr Przyby{\l}a

TL;DR
This paper argues that large language models like LaMDA and ChatGPT are not sentient or conscious, analyzing their architecture through Integrated Information Theory and addressing anthropomorphic language use in NLP.
Contribution
The paper provides a critical analysis of claims of sentience in LLMs, using Integrated Information Theory to argue that such models lack consciousness and clarifies ethical considerations.
Findings
LLMs do not exhibit signs of consciousness according to IIT
Claims of sentience are largely anthropomorphic projections
No significant architectural advances in LaMDA over similar models
Abstract
This work is intended as a voice in the discussion over previous claims that a pretrained large language model (LLM) based on the Transformer model architecture can be sentient. Such claims have been made concerning the LaMDA model and also concerning the current wave of LLM-powered chatbots, such as ChatGPT. This claim, if confirmed, would have serious ramifications in the Natural Language Processing (NLP) community due to wide-spread use of similar models. However, here we take the position that such a large language model cannot be sentient, or conscious, and that LaMDA in particular exhibits no advances over other similar models that would qualify it. We justify this by analysing the Transformer architecture through Integrated Information Theory of consciousness. We see the claims of sentience as part of a wider tendency to use anthropomorphic language in NLP reporting. Regardless…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Topic Modeling · Ethics and Social Impacts of AI
MethodsAttention Is All You Need · Softmax · Layer Normalization · Adam · Linear Layer · Dense Connections · Multi-Head Attention · Residual Connection · Byte Pair Encoding · Position-Wise Feed-Forward Layer
