In what sense does a large language model (LLM) have knowledge? We answer by
granting LLMs 'instrumental knowledge': knowledge gained by using next-word generation …

Abstract Language models are increasingly being deployed for general problem solving
across a wide range of tasks, but are still confined to token-level, left-to-right decision …

Text generation is of great importance to many natural language processing applications.
However, maximization-based decoding methods (eg, beam search) of neural language …

In this work, we aim to capitalize on the unique few-shot capabilities of large-scale language
models (LSLMs) to overcome some of their challenges with respect to grounding to factual …

Large language models can be prompted to pro-duce fluent output for a wide range of tasks
without being specifically trained to do so. Nevertheless, it is notoriously difficult to control …

Existing large language model-based code generation pipelines typically use beam search
or sampling algorithms during the decoding process. Although the programs they generate …

Large Language Models (LLMs) are frequently used for multi-faceted language generation
and evaluation tasks that involve satisfying intricate user constraints or taking into account …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Theorem proving in natural mathematical language–the mixture of symbolic and natural
language used by humans–plays a central role in mathematical advances and education …

Many high-level skills that are required for computer vision tasks, such as parsing questions,
comparing and contrasting semantics, and writing descriptions, are also required in other …