K-NN CLASSIFIER

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Findings

2023.findings-emnlp

local inexpensive model
local model
GPT-3.5
k-NN classifier
Multi-Layer Perceptron
cost
Prompting Large Language Models
creating large task-specific training datasets
intent recognition
sentiment analysis

2023

Language Models

1 papers

Cache me if you Can: an Online Cost-aware Teacher-Student framework to Reduce the Calls to Large Language Models (2023.findings-emnlp)

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Ilias Stogiannidis, Stavros Vassos, Prodromos Malakasiotis, Ion Androutsopoulos

Language Models

Challenge:	Prompting Large Language Models (LLMs) performs impressively in zero- and few-shot settings.
Approach:	They propose a framework that allows reducing calls to LLMs by caching previous LLM responses and using them to train a local inexpensive model on the SME side.
Outcome:	The proposed framework reduces calls to LLMs by caching previous LLM responses and using them to train a local inexpensive model on the SME side.

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