Add to ORKGLarge Language models (LLMs) possess the capability to engage In-context Learning (ICL) by leveraging a few demonstrations pertaining to a new downstream task as conditions. However, this particular learning paradigm suffers from high instability stemming from substantial variances induced by factors such as the input distribution of selected examples, their ordering, and prompt formats. In this work, we demonstrate that even when all these factors are held constant, the random selection of examples still results in high variance. Consequently, we aim to explore the informative ability of data examples by quantifying the Information Gain (IG) obtained in prediction after observing a given example candidate. Then we propose to sample those w...
In-context learning is a recent paradigm in natural language understanding, where a large pre-traine...
Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unsee...
In recent years, there has been significant progress in developing pre-trained language models for N...
In-context learning (ICL) i.e. showing LLMs only a few task-specific demonstrations has led to downs...
With a handful of demonstration examples, large-scale language models show strong capability to perf...
Through in-context learning (ICL), large-scale language models are effective few-shot learners witho...
In-Context Learning (ICL) over Large language models (LLMs) aims at solving previously unseen tasks ...
Language model fine-tuning is essential for modern natural language processing, but is computational...
Large language models (LMs) are able to in-context learn -- perform a new task via inference alone b...
When primed with only a handful of training samples, very large, pretrained language models such as ...
Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models le...
Pre-trained models of source code have gained widespread popularity in many code intelligence tasks....
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cl...
Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, whe...
Large language models have exhibited emergent abilities, demonstrating exceptional performance acros...
In-context learning is a recent paradigm in natural language understanding, where a large pre-traine...
Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unsee...
In recent years, there has been significant progress in developing pre-trained language models for N...
In-context learning (ICL) i.e. showing LLMs only a few task-specific demonstrations has led to downs...
With a handful of demonstration examples, large-scale language models show strong capability to perf...
Through in-context learning (ICL), large-scale language models are effective few-shot learners witho...
In-Context Learning (ICL) over Large language models (LLMs) aims at solving previously unseen tasks ...
Language model fine-tuning is essential for modern natural language processing, but is computational...
Large language models (LMs) are able to in-context learn -- perform a new task via inference alone b...
When primed with only a handful of training samples, very large, pretrained language models such as ...
Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models le...
Pre-trained models of source code have gained widespread popularity in many code intelligence tasks....
Prompt-based learning has shown considerable promise in reformulating various downstream tasks as cl...
Large language models (LMs) such as GPT-3 have the surprising ability to do in-context learning, whe...
Large language models have exhibited emergent abilities, demonstrating exceptional performance acros...
In-context learning is a recent paradigm in natural language understanding, where a large pre-traine...
Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unsee...
In recent years, there has been significant progress in developing pre-trained language models for N...