Apr 3, 2024 · In this paper, we propose a strategy to make LLMs as efficient as 0-shot text classifiers, while getting comparable or better accuracy than ICL.
May 20, 2024 · First, very few real data points are used to generate synthetic data using ICL. Then, the synthetic data is filtered using ICL by LLM again.
Using a single LLM and few-shot real data we perform a sequence of generation, filtering and Parameter-Efficient Fine-Tuning steps to create a robust and ...
Apr 3, 2024 · In this paper, we propose a strategy to make LLMs as efficient as 0-shot text classifiers, while getting comparable or better accuracy than ICL.
We augment the training data with synthetic data to ensure better training of. PEFT. ○ Our method has 3 steps: generate data, filter data, and train. Page 5 ...
In this paper, we proposea strategy to make LLMs as efficient as 0-shot text classifiers, while gettingcomparable or better accuracy than ICL.
Apr 24, 2024 · The framework has the following steps: 1) Generate data: use the examples and the LLM to generate synthetic data. 2) Filter data: remove ...
Apr 15, 2024 · Enhancing Low-Resource LLMs Classification with PEFT and Synthetic Data (https://t.co/NOi1AiCGLy) Listen to this paper as a podcast here: ...
Experiments in low resource settings show that augmenting the training material with the proposed strategy systematically improves the results on text ...
在本文中，我们提出了一种策略，使LLM像0-shot文本分类器一样高效，同时获得与ICL相当或更好的准确性。我们的解决方案针对低资源情况，即每类只有4个示例可用。使用单个LLM和 ...
Create synthetic data that replicates your production’s data underlying business logic. Synthetic data brings an end to critical bugs in production caused by incomplete testing. Data Privacy Guarantees. Stellar Support.