With the increasing ability of large language models (LLMs), in-context learning (ICL) has
become a new paradigm for natural language processing (NLP), where LLMs make …

Large language models (LMs) are able to in-context learn--perform a new task via inference
alone by conditioning on a few input-label pairs (demonstrations) and making predictions for …

Large language models have achieved impressive performance on various natural
language processing tasks. However, so far they have been evaluated primarily on …

Existing techniques for training language models can be misaligned with the truth: if we train
models with imitation learning, they may reproduce errors that humans make; if we train …

The past decade has witnessed dramatic gains in natural language processing and an
unprecedented scaling of large language models. These developments have been …

Large language models (LLMs) have demonstrated remarkable potential in handling
multilingual machine translation (MMT). In this paper, we systematically investigate the …

Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are
often publicly unavailable and their immense sizes make the models difficult to be tuned with …

We report the presence of a simple neural mechanism that represents an input-output
function as a vector within autoregressive transformer language models (LMs). Using causal …

In-context learning (ICL) emerges as a promising capability of large language models
(LLMs) by providing them with demonstration examples to perform diverse tasks. However …

The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have
excited the natural language and machine learning community over recent years. Despite of …