mlsanityData to 14 Oct 2026

Evolution of Natural Language Processing Technology: Not Just Language Processing Towards General Purpose AI

arXiv 2310.06228 ↗First posted 10 Oct 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

Since the invention of computers, communication through natural language (actual human language) has been a dream technology. However, natural language is extremely difficult to mathematically formulate, making it difficult to realize as an algorithm without considering programming. While there have been numerous technological developments, one cannot say that any results allowing free utilization have been achieved thus far. In the case of language learning in humans, for instance when learning one's mother tongue or foreign language, one must admit that this process is similar to the adage "practice makes perfect" in principle, even though the learning method is significant up to a point. Deep learning has played a central role in contemporary AI technology in recent years. When applied to natural language processing (NLP), this produced unprecedented results. Achievements exceeding the initial predictions have been reported from the results of learning vast amounts of textual data using deep learning. For instance, four arithmetic operations could be performed without explicit learning, thereby enabling the explanation of complex images and the generation of images from corresponding explanatory texts. It is an accurate example of the learner embodying the concept of "practice makes perfect" by using vast amounts of textual data. This report provides a technological explanation of how cutting-edge NLP has made it possible to realize the "practice makes perfect" principle. Additionally, examples of how this can be applied to business are provided. We reported in June 2022 in Japanese on the NLP movement from late 2021 to early 2022. We would like to summarize this as a memorandum since this is just the initial movement leading to the current large language models (LLMs).

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References · 20

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2020Scaling Laws for Neural Language Modelscited by 6,586
  6. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  7. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  8. 2017Deep reinforcement learning from human preferencescited by 3,912
  9. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,795
  10. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
  11. 2022Solving Quantitative Reasoning Problems with Language Modelscited by 1,392
  12. 2021MetaFormer Is Actually What You Need for Visioncited by 524
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  1. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 511
  2. 2015Deep Spatial Autoencoders for Visuomotor Learningcited by 153
  3. 2021What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformerscited by 76
  4. 2020A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systemscited by 71
  5. 2022HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot Learningcited by 55
  6. 2017Compositional Falsification of Cyber-Physical Systems with Machine Learning Componentscited by 32
  7. 2019Guaranteeing Safety for Neural Network-Based Aircraft Collision Avoidance Systemscited by 26
  8. 2021Causal Analysis of Agent Behavior for AI Safetycited by 4

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