48 lines
2.3 KiB
Markdown
48 lines
2.3 KiB
Markdown
file:: [Ozkaya - 2023 - Application of Large Language Models to Software E.pdf](file://C:/Users/david/Zotero/storage/WHX3PLDV/Ozkaya - 2023 - Application of Large Language Models to Software E.pdf)
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file-path:: file://C:/Users/david/Zotero/storage/WHX3PLDV/Ozkaya - 2023 - Application of Large Language Models to Software E.pdf
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- not to mention software engineers
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 65082008-a809-498c-aafb-913403fc7463
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- Recently released LLMs, such as Generative Pretrained Transformer(GTP) 4 used in ChatGPT by OpenAI and BERT used in Bard by Google, disrupt the search engine model that we have been used to.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: blue
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id:: 65082aeb-6c17-47ac-b388-90ea6f5c361a
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- An LLM is a deep neural network model which has been trained on large amounts of data, such as books, code, articles, and websites, to learn the underlying patterns and relationships in the language that it was trained for.
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ls-type:: annotation
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hl-page:: 1
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hl-color:: purple
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id:: 65082b88-cec6-4393-af1d-3f5b933fac38
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- the model is able to generate coherent content such as grammatically correct sentences and paragraphs that mimic human language or syntactically correct code snippets
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ls-type:: annotation
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hl-page:: 1
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hl-color:: green
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id:: 65199d20-c3da-4dac-89e5-b51167b719b2
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- While the content generated by LLMs are often grammatically correct, they may not always be semantically correct.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: blue
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id:: 65199d44-84bc-4ed0-924e-874a4f8173be
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- The probabilistic and randomized selection of the “next token” in constructing the outputs on one hand gives the end user the impressions of correctness and style, on the other hand may result in mistakes.
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 65199da0-f6b8-495d-a04d-07f8bef810e2
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- Data quality and bias concerns:
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ls-type:: annotation
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hl-page:: 2
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hl-color:: blue
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id:: 65199dc4-a368-4507-b421-6cc3f772f621
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- Any of the issues that exist in the training data, such as biases and mistakes, will be amplified by LLMs
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ls-type:: annotation
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hl-page:: 2
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hl-color:: green
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id:: 65199ed1-141f-4836-b26f-2da8c1687791
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- making prejudiced recommendations
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ls-type:: annotation
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hl-page:: 2
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hl-color:: purple
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id:: 65199ee0-a186-43d2-a8df-7d3eabbb7498 |