Meta’s new model, Toolformer, introduces a novel approach to overcoming the limitations of large language models (LLMs) by enabling them to leverage tools via APIs. This capability addresses issues such as accessing real-time information, reducing factual inaccuracies, and improving performance in low-resource languages and mathematical tasks.
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Strategies for Reducing Costs in Large Language Model API Usage
Insights from Frugal GPT Paper
The escalating costs associated with LLM APIs necessitate efficient strategies to manage and optimize their usage without compromising performance. The following strategies, derived from the “Frugal GPT” paper, can serve as an excellant guide to save on LLM API usage cost.
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Introduction to gRPC
Evolution of RPCs to gRPC
Evolution
Understanding how RPC protocols have evolved may help us understand the context better.
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OpenPrompt- A Prompt-learning Framework
Prompt Tuning
One core idea of prompt-learning is to use additional context with masked tokens to imitate the pre-training objectives of PLMs and better stimulate these models.
Hence, the choice of PLMs is crucial to the whole pipeline of prompt-learning.
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Structural Probing
Does word representations encode syntactic information?
Hypothesis
Do the language modelling objective implicitly encode/learn the entire parse tree?
Can I detect a path from the root of the
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