Is this robust to prompts that request specific writing styles, through either instruction or k-shot demonstration of human writing?
LLMS
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Generative AI Impact: Images vs Text Which Will Dominate
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Which type of Generative AI do you think will have a bigger commercial impact: Generating images (e.g., diffusion algorithms, stable diffusion) or text (e.g., LLMs, ChatGPT)?
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Selecting Cohere Classification Models for Email Tasks
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Step 3: Select the appropriate model for your usecase. Cohere's classify end-point has three models: 1. small
2. large
3. multilingual For our email classification task, we use the large model. -
Generate Synthetic Data Using Cohere’s API Endpoint
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Pro tip You can even use Cohere's generate endpoint to get synthetic data (emails in our case) on-the-fly.
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Cohere Classify Endpoint: Text Classification Tool Guide
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Step 1: Go to the classify endpoint in the cohere playground. Here is the link https://
dashboard.cohere.ai/welcome/regist
er?utm_source=influencer&utm_medium=social&utm_campaign=shubham
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Prompt Engineering as Systems Engineering: LLM Workflow Abstraction
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“Prompt engineering looks more like a systems engineering problem, not a machine learning one.” i like the concept of an “LLM friendly workflow engine”. concats all my recent professional interests into one sorely needed abstraction
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LLM watermark detection: 28 tokens vs expected 9, odds 6e-14
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In this sample from the paper, text written by a human would likely contain a certain number of "whitelisted" tokens (expected to be about 9 in this sample), but the text produced by the LLM with the watermark contains 28. Odds of this happening randomly = 6 × 10^−14.
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Watermarking detects AI-generated text for readers and academia
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These cryptic text signatures are indiscernible to the average reader, but reliably detectable by the algorithm. Watermarking can alert you when you're reading a web article that was AI generated (sorry, CNET) or be used in academia to detect "AI Plagiarism"
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Watermark selects whitelist tokens and promotes them during sampling
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The watermark works by selecting a randomized set of “whitelist” tokens before any text is generated, and then softly promoting use of those tokens during sampling. (a token can be a letter, word, numbers, punctuations or any other string that is useful for the specific task)