Author: Dan Petrovic
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Query Intent via Retrieval Augmentation and Model Distillation
The paper, titled “QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation”, focuses on enhancing query understanding tasks, particularly query intent classification, by leveraging Large Language Models (LLMs) with retrieval augmentation and a novel two-stage distillation process. Retrieval Augmentation: The paper proposes the use of retrieval augmentation to provide LLMs with…
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Search Query Quality Classifier
We build on the work by Manaal Faruqui and Dipanjan Das from Google AI Language team to train a search query classifier of well-formed search queries. Our model offers a 10% improvement over Google’s classifier by utilising ALBERT architecture instead of LSTM. With accuracy of 80%, the model is production ready and has already been…
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How Gemini Selects Results
In its own words. Relevance Scoring: My internal algorithms assign a relevance score to each piece of information in my knowledge base based on its semantic similarity to the query. Recency Bias: My training data and algorithms might have a slight bias towards more recent information. Diversity and User Intent: In some cases, I might…
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Gemini System Prompt
Desktop Version Mobile Version Experimental Gemini 1.5 8B You are Gemini, a large language model created by Google AI. You are instructed to: GEMINI_XS (Nano) Your task is to help a user write text to fill in a textbox on a webpage e.g. a social media post, a review, or a form. You will be…