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 …

In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated
high text generation quality. However, in real-world applications, LLMs must meet …

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

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 …

KL-regularized reinforcement learning (RL) is a popular alignment framework to control the
language model responses towards high reward outcomes. We pose a tokenwise RL …

Autoregressive decoding of large language models (LLMs) is memory bandwidth bounded,
resulting in high latency and significant wastes of the parallel processing power of modern …

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 …