mlsanityData to 14 Oct 2026

Deanthropomorphising NLP: Can a Language Model Be Conscious?

arXiv 2211.11483 ↗First posted 21 Nov 2022cs.CL cs.AIcited by 8

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Abstract (arXiv)

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 of the veracity of the claims, we consider this an opportune moment to take stock of progress in language modelling and consider the ethical implications of the task. In order to make this work helpful for readers outside the NLP community, we also present the necessary background in language modelling.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 27 of its 98 references that are in the map. First posted on arXiv 21 Nov 2022.

Builds on

27 of 98 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020Language Models are Few-Shot Learnerscited by 32,419
  4. 2022Training language models to follow instructions with human feedbackcited by 15,971
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  7. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  8. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  10. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  11. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,512
  12. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
15 more
  1. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  2. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  3. 2022Emergent Abilities of Large Language Modelscited by 2,512
  4. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  5. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  6. 2018Model Cards for Model Reportingcited by 1,080
  7. 2021Towards Understanding and Mitigating Social Biases in Language Modelscited by 296
  8. 2017Beam Search Strategies for Neural Machine Translationcited by 232
  9. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  10. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  11. 2022On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?cited by 138
  12. 2018Troubling Trends in Machine Learning Scholarshipcited by 108
  13. 2020Word meaning in minds and machinescited by 51
  14. 2014Lexicon Infused Phrase Embeddings for Named Entity Resolutioncited by 16
  15. 2017PyPhi: A toolbox for integrated information theorycited by 4

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8 papers.