Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Deploying large language models (LLMs) is challenging because they are memory
inefficient and compute-intensive for practical applications. In reaction, researchers train …

BIG-Bench (Srivastava et al., 2022) is a diverse evaluation suite that focuses on tasks
believed to be beyond the capabilities of current language models. Language models have …

We explore how generating a chain of thought---a series of intermediate reasoning steps---
significantly improves the ability of large language models to perform complex reasoning. In …

Language Models (LMs) can perform new tasks by adapting to a few in-context examples.
For humans, explanations that connect examples to task principles can improve learning …

Humans (eg, crowdworkers) have a remarkable ability in solving different tasks, by simply
reading textual instructions that define them and looking at a few examples. Despite the …

While language models are powerful and versatile, they often fail to address highly complex
problems. This is because solving complex problems requires deliberate thinking, which has …

Chain-of-thought prompting (eg," Let's think step-by-step") primes large language models to
verbalize rationalization for their predictions. While chain-of-thought can lead to dramatic …

Few-shot NLP research is highly active, yet conducted in disjoint research threads with
evaluation suites that lack challenging-yet-realistic testing setups and fail to employ careful …

As the use of deep learning techniques has grown across various fields over the past
decade, complaints about the opaqueness of the black-box models have increased …