Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

Abstract Large Language Models (LLMs) have demonstrated impressive performance in
various NLP tasks, but they still suffer from challenges such as hallucination and weak …

Large language models (LLMs) have demonstrated an impressive ability to perform
arithmetic and symbolic reasoning tasks, when provided with a few examples at test time (" …

Given the recent impressive accomplishments of language models (LMs) for code
generation, we explore the use of LMs as general adaptive mutation and crossover …

While Chain-of-Thought (CoT) prompting boosts Language Models'(LM) performance on a
gamut of complex reasoning tasks, the generated reasoning chain does not necessarily …

Large language models like GPT-4 exhibit emergent capabilities across general-purpose
tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks …

Large language models (LLMs) can capture rich representations of concepts that are useful
for real-world tasks. However, language alone is limited. While existing LLMs excel at text …

Chain-of-thought prompting (CoT) and tool augmentation have been validated in recent
work as effective practices for improving large language models (LLMs) to perform step-by …

End-to-end neural Natural Language Processing (NLP) models are notoriously difficult to
understand. This has given rise to numerous efforts towards model explainability in recent …