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Author ORCID Identifier
https://orcid.org/0000-0001-6574-0817
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Computer Science
Year Degree Awarded
2023
Month Degree Awarded
September
First Advisor
Mohit Iyyer
Subject Categories
Artificial Intelligence and Robotics | Data Science | Information Security
Abstract
Text generation is an important emerging AI technology that has seen significant research advances in recent years. Due to its closeness to how humans communicate, mastering text generation technology can unlock several important applications such as intelligent chat-bots, creative writing assistance, or newer applications like task-agnostic few-shot learning. Most recently, the rapid scaling of large language models (LLMs) has resulted in systems like ChatGPT, capable of generating fluent, coherent and human-like text. However, despite their remarkable capabilities, LLMs still suffer from several limitations, particularly when generating long-form text. In particular, (1) long-form generated text is filled with factual inconsistencies to world knowledge and the input prompt; (2) it is difficult to accurately evaluate the quality of long-form generated text; (3) it is difficult to identify whether a piece of long-form text was AI-generated, a task necessary to prevent widespread misinformation and plagiarism. In this thesis I design algorithms aimed at making progress towards these three issues in current LLMs. I will first describe a retrieval-augmented system we built for long-form question answering, to improve factual correctness of long-form generated text. However, a careful empirical analysis reveals issues related to input/output consistency of generated text, and an inherent difficulty in evaluation. I will then describe our model RankGen, which uses large-scale contrastive learning on documents to significantly outperform competing long-form text generation methods to generate text more faithful to the input. Next, I will describe our efforts to improve human evaluation of long-form generation (issue #2) by proposing the LongEval guidelines. LongEval is a set of three simple empirically-motivated ideas to make human evaluation of long-form generation more consistent, less expensive, and cognitively easier for evaluators. Finally, I describe my work on AI-generated text detection (issue #3), and showcase the brittleness of existing methods to paraphrasing attacks I designed. I will describe a simple new AI-generated text detection algorithm using information retrieval, which is significantly more robust to paraphrasing attacks. Finally, I conclude this thesis with some future research directions that I am excited about, including plan-based long-form text generation, and a deeper dive into understanding large language model training dynamics.
DOI
https://doi.org/10.7275/35984373
Recommended Citation
Krishna, Kalpesh, "Towards Robust Long-form Text Generation Systems" (2023). Doctoral Dissertations. 3004.
https://doi.org/10.7275/35984373
https://scholarworks.umass.edu/dissertations_2/3004
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Information Security Commons