AI Coder - Remark Labelling

AI Coder - Remark Labelling

Introduction

When evaluating products, it is important to ask panellists to provide argument-specific answers or to write down what they think about a product in their own words. However, making sense of a large dataset in which individual comments were collected may be difficult. Traditionally, remark labeling has been a manual process involving the creation and assignment of labels to corresponding remarks. While effective to some degree, this method can be time-consuming. Additionally, as data volumes grow, manually labeling remarks becomes increasingly laborious.
To address this requirement, an innovative feature has emerged: the AI Coder for Remark Labeling. This cutting-edge innovation promises to transform how notes are processed and sorted, enhancing productivity and saving time through the use of AI. 

How it Works

  1. Summary Generation: a Large Language Model(LLM) are used on a subset of remarks to provide a summary of the general themes present.
  2. Individual Labeling: a Large Language Model(LLM), combined with a predefined set of labels (such as "sweet" or "sour"), labels each remark individually. Users can choose from our provided label sets or create their own custom sets.
  3. Sentiment Analysis: a Large Language Model(LLM) always assigns one of three sentiment-based labels to each remark: bad, neutral, or good.
At this moment we use GPT(Generative Pre-trained Transformer) as our default LLM, the GPT-N series is developed by OpenAI and is based on a deep learning architecture called the Transformer.
Here's a simplified explanation of how GPT works:
  1. Data Collection: GPT is trained on a large dataset of text from various sources on the internet. This dataset contains a wide range of topics and writing styles, providing the model with a diverse understanding of language.
  2. Tokenization: When you input text into GPT, it first breaks down your input into smaller chunks called tokens. These tokens are the basic units of text that the model processes. On average 1000 words will be 750 tokens. 
  3. Contextual Understanding: GPT then uses its deep learning architecture to analyze the tokens in the context of each other. Unlike older language models, GPT models can understand and generate text based on the broader context of the conversation, not just the immediate previous message.
  4. Prediction: Based on its understanding of the context provided by the input tokens, GPT generates a response by predicting the most probable next tokens in the conversation. It does this by assigning probabilities to each token in its vocabulary and selecting the most likely ones based on the context.
  5. Generation: Finally, GPT generates a response by selecting the tokens with the highest probabilities. These tokens are then assembled into coherent text and returned as the model's response to the user's input.
  6. Iterative Learning: The model continually improves through a process called fine-tuning. This involves training the model on additional data and fine-tuning its parameters to improve its performance on specific tasks or domains.
Overall, GPT works by understanding the context of a conversation and generating text based on that understanding, leveraging its extensive training on a large dataset of diverse text from the internet.

Security 

OpenAI places a high priority on data security and privacy. According to the information provided in the extracts:
  1. Data sent to the OpenAI API is not used to train or improve OpenAI models(https://platform.openai.com/docs/models). 
  2. Data submitted through the OpenAI API is not used to improve OpenAI’s service offering (https://openai.com/security/).
  3. OpenAI does not share user content with third parties for marketing purposes (https://openai.com/security/).
  4. To help identify abuse, API data may be retained for up to 30 days, after which it will be deleted (unless otherwise required by law). (https://platform.openai.com/docs/models).
Therefore, your data’s security and privacy should not be compromised when using the OpenAI API. However, it’s always a good practice to review the Privacy Policy (https://openai.com/en-GB/policies/eu-privacy-policy/) and Terms of Use (https://openai.com/policies/terms-of-use/) to ensure you understand how your data is handled.

NOTE:
  1. Remark or text question's in EyeQuestion that were flagged to be Pii information will not be sent to OpenAI.
  2. Product information that is setup in EyeQuestion will not be sent to OpenAI. 
  3. All remarks selected are sent to OpenAI for processing. To protect personal information, you can exclude them from the analysis specific remarks where panellist have included personal information. 

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