Skip to main content

2023-05 Reading Paper

2023-05-05โ€‹

These two papers mainly discuss the development history and framework introduction of large language models. For friends who are not familiar with machine learning, it may be a bit difficult to read, but actually you don't need to understand all the concepts. Just skim through it once. In addition, I personally feel that understanding the model framework can help understand why the prompt is written that way.

2023-05-06โ€‹

  • ShapยทE: Generating Conditional 3D Implicit FunctionsThe Practical Guides for Large Language Models.
  • The Elephant in the Room: This paper mainly uses a corpus with comprehensive metadata of 78,187 NLP publications and 701 resumes of NLP publication authors, to explore the industry presence in the field since the early 90s. The authors found that the number of authors with industry affiliations in NLP has been steady before a steep increase over the past five years (180% growth from 2017 to 2022). They also found in recent years, some internet companies have published a large number of papers (Jimmy: I've been reading more and more AI related papers recently, and I feel that in the LLMs era, research and engineering are deeply integrated).

2023-05-07โ€‹