UC Irvine

In the past several years, the field of natural language processing has undergone a paradigm shift driven by a singular piece of technology: large language models. Large language models have emerged as fundamental tools in our field, significantly advancing the state of the art and enabling breakthroughs across a wide range of tasks. The capabilities of large language models are an emergent function of scale - they are trained on terabytes of text data scraped from all corners of the web. By virtue of this training, large language models capture a rough notion of the factual state of the world in their weights. Understanding how language models represent these facts and how these representations are used to generate text is vital given their increasing deployment in the world. In this dissertation, our goal is to provide tooling and technologies to probe and evaluate how language models leverage and generate facts and correct the cases where language models produce inaccurate factual statements.

This dissertation is split into three distinct parts. The first is concerned with evaluating the generations of large language models for correctness. Here, we develop metrics (i.e., LERC) for evaluating the outputs of language models. The second is concerned with characterizing how language models leverage different sources of facts and use them during generation. We develop benchmarks (i.e., AmbER and knowledge conflicts) for assessing the tendencies of language models to over-rely on information stored in their weights and connect this to their propensity to generate factually inaccurate statements. Our final part is concerned with correcting inaccuracies in language model generations. We develop methodologies for training models for correcting factual inaccuracies that language models generate (i.e., RARR and PURR) as well as concretely establish the task of editing for factuality by introducing new evaluation benchmarks and metrics.

In total, the work presented in this dissertation provides a concrete way to evaluate the facts language models generate, characterize how language models leverage different sources of facts and use them during generation, and correct the facts language models generate.