In recent years, Large Language Model (LLM) have seen significant advancements, attracting attention for their applications in various fields. These models have shown promising results in handling tabular data,
在 NLP 领域，通过模型蒸馏可用大型 LLM 增强小型 LLM 的能力；在基于 LLM 的代理中，借助合成代码和...
But rather than having clinicians rewrite each other’s notes to help achieve this – which would create undue burdens on already busy care teams – the research team turned to large language models (LLMs). “Given a specific note that we wish to rewrite in the style of...
数据增广(data augmentation)能够有效解决低资源(low resource)场景下的数据稀缺性。然而,当应用到字符级(token-level)任务上时,例如命名实体识别(NER),数据增广方法通常会遭遇字符与标签存在偏差(如图2a,2b所示),导致了不满意的性能表现。所以这篇文章提出了Masked Entity Language Modeling(MELM)模型作为一个新颖的数...
These models are extensively used as generative AI tools for data augmentation but data security and privacy remain a fundamental concern associated with LLM model in the digital domain. Traditional security approach shows potential challenges in addressing emerging threats such as adversarial attacks, ...
Data augmentation:Augment public data with private knowledge corpus providing application-specific engagement. What are the potential challenges and limitations of LlamaIndex? While LlamaIndex offers powerful capabilities in data indexing and retrieval, it's important to be aware of its potential challenges...
To address it, we propose a KBQG method via data augmentation with dynamic-prompt (DADP), which leverages the semantic similarity of relational paths across different subgraphs. We prompt the large language model (LLM) to generate questions for data augmentation by utilizing our dynamic-prompt ...
How do we best augment LLMs with our own private data? We need a comprehensive toolkit to help perform this data augmentation for LLMs. Proposed Solution That's whereLlamaIndexcomes in. LlamaIndex is a "data framework" to help you build LLM apps. It provides the following tools: ...
spelling_error_word: false # whether to open the augmentation method of simulating the spelling error for words in the original texts. e.g. "I love LLM" --> "Ai love LLM" split_random_word: false # whether to open the augmentation method of splitting words randomly with whitespaces in ...
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework (DAC 2024) 深度学习发展的过程中,模型和数据是个对称的概念,而深度学习发展按照优化的思路可以分为两类: Model-centric:根据领域知识来优化模型架构。