In-context learning是LLM最重要的emergent ability之一,它可以在模型inference阶段通过上下文的内容学习,无需梯度下降,可以在文本分类等任务达到很高的准确率。以图中的单词分类case为例,"foo"和"bar"是semantic-unrelated label,分别对应country和animal。图中的语句为"France : bar Cat : foo Dog :",demonstration是...
What is in-context ability? 能够从context中找到similar pattern, 并且repeat. 首先要理解的一个重要问题是:in-context ability 与以往的pattern repeat 有何区别? Q: What is in-context learning? Difference with statistical correlation in training set? 这个能力与以往根据statistic frequency进行关联是不一样的...
比起小模型,大模型有一个很重要的涌现能力(Emergent ability)就是In-Context Learning(ICL),也是一种新的范式,指在不进行参数更新的情况下,只在输入中加入几个示例就能让模型进行学习,如下图中用ICL做情感分析任务的栗子: 忽略大模型的贵,这个范式具备不少优势: 输入的形式是自然语言,可以让我们可以更好地跟语言...
learning of Transformers. The effectiveness of these strategies is further confirmed in natural language processing tasks. In conclusion, our research demonstrates the feasibility of cultivating a powerful in-context learning ability within AI systems in an eco-friendly manne...
In addition, UniLog can further enhance its logging ability after warmup with only a few hundred random samples. We evaluated UniLog on a large dataset containing 12,012 code snippets extracted from 1,465 GitHub repositories. The results show that UniLog achieved the state-of-the-art performa...
🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability. otter-ntu.github.io/ Topics machine-learning deep-learning multi-modality artificial-inteligence ...
learning efficiency, and task-conditional probabilities, which can help us understand how model behaviour changes under alignment. We use these to show how ICL ability varies at different model scales, understand how finetuning harms knowledge of disfavoured distributions, and compare base and instructi...
Improving In-Context Few-Shot Learning via Self-Supervised Training.. Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, Zornitsa Kozareva. [pdf], [project], 2022.5, Calibrate Before Use: Improving Few-shot Performance of Language Models.. ...
The predictions of Large Language Models (LLMs) on downstream tasks often improve significantly when including examples of the input--label relationship in the context. However, there is currently no consensus about how this in-context learning (ICL) ability of LLMs works. For example, while ...
In-context learning refers to the emerging ability of large language models (LLMs) to perform a target task without additional training, utilizing demonstrations of the task. Recent studies aim to enhance in-context learning performance by selecting more useful demonstrations. However, they overlook ...