Self-correction is an approach to improving responses from large language models (LLMs)
by refining the responses using LLMs during inference. Prior work has proposed various self …

This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

Like humans, large language models (LLMs) do not always generate the best output on their
first try. Motivated by how humans refine their written text, we introduce Self-Refine, an …

Large language models (LLMs) have been increasingly used to interact with external
environments (eg, games, compilers, APIs) as goal-driven agents. However, it remains …

Large language models (LLMs) pretrained on vast source code have achieved prominent
progress in code intelligence. However, existing code LLMs have two main limitations in …

Recent developments in large language models (LLMs) have been impressive. However,
these models sometimes show inconsistencies and problematic behavior, such as …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

The task of generating code solutions for a given programming problem can benefit from the
use of pre-trained language models such as Codex, which can produce multiple diverse …

Recent efforts have incorporated large language models (LLMs) with external resources (eg,
the Internet) or internal control flows (eg, prompt chaining) for tasks requiring grounding or …

The task of generating code from a natural language description, or NL2Code, is considered
a pressing and significant challenge in code intelligence. Thanks to the rapid development …