Add to ORKGVery large language models (LLMs), such as GPT-3 and Codex have achieved state-of-the-art performance on several natural-language tasks, and show great promise also for code. A particularly exciting aspect of LLMs is their knack for few-shot and zero-shot learning: they can learn to perform a task with very few examples. Few-shotting has particular synergies in software engineering, where there are a lot of phenomena (identifier names, APIs, terminology, coding patterns) that are known to be highly project-specific. However, project-specific data can be quite limited, especially early in the history of a project; thus the few-shot learning capacity of LLMs might be very relevant. In this paper, we investigate the use few-shot training with ...
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural langua...
Few-shot classification requires deep neural networks to learn generalized representations only from...
Scaling language models with more data, compute and parameters has driven significant progress in na...
Pretrained large language models (LLMs) are widely used in many sub-fields of natural language proce...
Few-shot learning with large-scale, pre-trained language models is a powerful way to answer question...
Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these ...
Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineer...
In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension a...
In many machine learning tasks, the available training data has a skewed distribution- a small set o...
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown e...
We present a new method LiST is short for Lite Prompted Self-Training for parameter-efficient fine-t...
Recently, there has been an increasing interest in models that generate natural language explanation...
Large language models (LMs) of code have recently shown tremendous promise in completing code and sy...
Deploying large language models (LLMs) is challenging because they are memory inefficient and comput...
Domain-specific text classification faces the challenge of scarce labeled data due to the high cost ...
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural langua...
Few-shot classification requires deep neural networks to learn generalized representations only from...
Scaling language models with more data, compute and parameters has driven significant progress in na...
Pretrained large language models (LLMs) are widely used in many sub-fields of natural language proce...
Few-shot learning with large-scale, pre-trained language models is a powerful way to answer question...
Large-scale generative language models such as GPT-3 are competitive few-shot learners. While these ...
Large Language Models (LLM) are a new class of computation engines, "programmed" via prompt engineer...
In this work, we evaluate 10 open-source instructed LLMs on four representative code comprehension a...
In many machine learning tasks, the available training data has a skewed distribution- a small set o...
When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown e...
We present a new method LiST is short for Lite Prompted Self-Training for parameter-efficient fine-t...
Recently, there has been an increasing interest in models that generate natural language explanation...
Large language models (LMs) of code have recently shown tremendous promise in completing code and sy...
Deploying large language models (LLMs) is challenging because they are memory inefficient and comput...
Domain-specific text classification faces the challenge of scarce labeled data due to the high cost ...
Pretrained language models (PLMs) have demonstrated remarkable performance in various natural langua...
Few-shot classification requires deep neural networks to learn generalized representations only from...
Scaling language models with more data, compute and parameters has driven significant progress in na...