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 …

Generating accurate SQL from natural language questions (text-to-SQL) is a long-standing
challenge due to the complexities in user question understanding, database schema …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

This survey reviews works in which language models (LMs) are augmented with reasoning
skills and the ability to use tools. The former is defined as decomposing a potentially …

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

The advent of large language models trained on code (code LLMs) has led to significant
progress in language-to-code generation. State-of-the-art approaches in this area combine …

We introduce DS-1000, a code generation benchmark with a thousand data science
problems spanning seven Python libraries, such as Numpy and Pandas. Compared to prior …

We evaluate the reasoning abilities of large language models in multilingual settings. We
introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating …

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 …

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a
natural language prompt that demonstrates how to perform the task and no additional …