Deanthropomorphising NLP: Can a Language Model Be Conscious?

• URL: http://arxiv.org/abs/2211.11483v4
• Date: Wed, 15 Nov 2023 16:21:58 GMT
• Title: Deanthropomorphising NLP: Can a Language Model Be Conscious?
• Authors: Matthew Shardlow and Piotr Przyby{\l}a
• Abstract summary: 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 see the claims of sentience as part of a wider tendency to use anthropomorphic language in NLP reporting.
• Score: 19.390107818044736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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.

Related papers

• Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024) [16.282850445579857]
  Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically. Recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities. Despite ongoing discussions about what reasoning is in language models, it is still not easy to pin down to what extent these models are actually capable of reasoning.
  arXiv  Detail & Related papers  (2024-10-07T02:31:47Z)
• mALBERT: Is a Compact Multilingual BERT Model Still Worth It? [5.2116647104135305]
  We propose to focus on smaller models, such as compact models like ALBERT, which are more virtuous than these PLMs. PLMs enable huge breakthroughs in Natural Language Processing tasks, such as Spoken and Natural LanguageUnderstanding, classification, Question-Answering tasks. Considering these facts, wepropose the free release of the first version of a multilingual compact ALBERT model, pre-trained using Wikipediadata.
  arXiv  Detail & Related papers  (2024-03-27T08:25:28Z)
• Let Models Speak Ciphers: Multiagent Debate through Embeddings [84.20336971784495]
  We introduce CIPHER (Communicative Inter-Model Protocol Through Embedding Representation) to address this issue. By deviating from natural language, CIPHER offers an advantage of encoding a broader spectrum of information without any modification to the model weights. This showcases the superiority and robustness of embeddings as an alternative "language" for communication among LLMs.
  arXiv  Detail & Related papers  (2023-10-10T03:06:38Z)
• Transparency Helps Reveal When Language Models Learn Meaning [71.96920839263457]
  Our systematic experiments with synthetic data reveal that, with languages where all expressions have context-independent denotations, both autoregressive and masked language models learn to emulate semantic relations between expressions. Turning to natural language, our experiments with a specific phenomenon -- referential opacity -- add to the growing body of evidence that current language models do not well-represent natural language semantics.
  arXiv  Detail & Related papers  (2022-10-14T02:35:19Z)
• Masked Part-Of-Speech Model: Does Modeling Long Context Help Unsupervised POS-tagging? [94.68962249604749]
  We propose a Masked Part-of-Speech Model (MPoSM) to facilitate flexible dependency modeling. MPoSM can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction. We achieve competitive results on both the English Penn WSJ dataset and the universal treebank containing 10 diverse languages.
  arXiv  Detail & Related papers  (2022-06-30T01:43:05Z)
• Testing the Ability of Language Models to Interpret Figurative Language [69.59943454934799]
  Figurative and metaphorical language are commonplace in discourse. It remains an open question to what extent modern language models can interpret nonliteral phrases. We introduce Fig-QA, a Winograd-style nonliteral language understanding task.
  arXiv  Detail & Related papers  (2022-04-26T23:42:22Z)
• Language Models are not Models of Language [0.0]
  Transfer learning has enabled large deep learning neural networks trained on the language modeling task to vastly improve performance. We argue that the term language model is misleading because deep learning models are not theoretical models of language.
  arXiv  Detail & Related papers  (2021-12-13T22:39:46Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• Schr\"odinger's Tree -- On Syntax and Neural Language Models [10.296219074343785]
  Language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities. We observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form. We outline the implications of the different types of research questions exhibited in studies on syntax.
  arXiv  Detail & Related papers  (2021-10-17T18:25:23Z)
• PIGLeT: Language Grounding Through Neuro-Symbolic Interaction in a 3D World [86.21137454228848]
  We factorize PIGLeT into a physical dynamics model, and a separate language model. PIGLeT can read a sentence, simulate neurally what might happen next, and then communicate that result through a literal symbolic representation. It is able to correctly forecast "what happens next" given an English sentence over 80% of the time, outperforming a 100x larger, text-to-text approach by over 10%.
  arXiv  Detail & Related papers  (2021-06-01T02:32:12Z)
• Neural Models for Offensive Language Detection [0.0]
  Offensive language detection is an ever-growing natural language processing (NLP) application. We believe contributing to improving and comparing different machine learning models to fight such harmful contents is an important and challenging goal for this thesis.
  arXiv  Detail & Related papers  (2021-05-30T13:02:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.