ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Mitra, Arindam
Description
Humans have the remarkable ability to solve different tasks by simply reading textual instructions that define the tasks and looking at a few examples. Natural Language Processing (NLP) models built with the conventional machine learning paradigm, however, often struggle to generalize across tasks (e.g., a question-answering system cannot solve classification tasks) despite training with lots of examples. A long-standing challenge in Artificial Intelligence (AI) is to build a model that learns a new task by understanding the human-readable instructions that define it. To study this, I led the development of NATURAL INSTRUCTIONS and SUPERNATURAL INSTRUCTIONS, large-scale datasets of diverse tasks, their human-authored instructions, and instances. I adopt generative pre-trained language models to encode task-specific instructions along with input and generate task output. Empirical results in my experiments indicate that the instruction-tuning helps models achieve cross-task generalization. This leads to the question: how to write good instructions? Backed by extensive empirical analysis on large language models, I observe important attributes for successful instructional prompts and propose several reframing techniques for model designers to create such prompts. Empirical results in my experiments show that reframing notably improves few-shot learning performance; this is particularly important on large language models, such as GPT3 where tuning models or prompts on large datasets is expensive. In another experiment, I observe that representing a chain of thought instruction of mathematical reasoning questions as a program improves model performance significantly. This observation leads to the development of a large scale mathematical reasoning model BHASKAR and a unified benchmark LILA. In case of program synthesis tasks, however, summarizing a question (instead of expanding as in chain of thought) helps models significantly. This thesis also contains the study of instruction-example equivalence, power of decomposition instruction to replace the need for new models and origination of dataset bias from crowdsourcing instructions to better understand the advantages and disadvantages of instruction paradigm. Finally, I apply the instruction paradigm to match real user needs and introduce a new prompting technique HELP ME THINK to help humans perform various tasks by asking questions.
ContributorsMishra, Swaroop (Author) / Baral, Chitta (Thesis advisor) / Mitra, Arindam (Committee member) / Blanco, Eduardo (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2023
Description
While in recent years deep learning (DL) based approaches have been the popular approach in developing end-to-end question answering (QA) systems, such systems lack several desired properties, such as the ability to do sophisticated reasoning with knowledge, the ability to learn using less resources and interpretability. In this thesis, I explore solutions that aim to address these drawbacks.
Towards this goal, I work with a specific family of reading comprehension tasks, normally referred to as the Non-Extractive Reading Comprehension (NRC), where the given passage does not contain enough information and to correctly answer sophisticated reasoning and ``additional knowledge" is required. I have organized the NRC tasks into three categories. Here I present my solutions to the first two categories and some preliminary results on the third category.
Category 1 NRC tasks refer to the scenarios where the required ``additional knowledge" is missing but there exists a decent natural language parser. For these tasks, I learn the missing ``additional knowledge" with the help of the parser and a novel inductive logic programming. The learned knowledge is then used to answer new questions. Experiments on three NRC tasks show that this approach along with providing an interpretable solution achieves better or comparable accuracy to that of the state-of-the-art DL based approaches.
The category 2 NRC tasks refer to the alternate scenario where the ``additional knowledge" is available but no natural language parser works well for the sentences of the target domain. To deal with these tasks, I present a novel hybrid reasoning approach which combines symbolic and natural language inference (neural reasoning) and ultimately allows symbolic modules to reason over raw text without requiring any translation. Experiments on two NRC tasks shows its effectiveness.
The category 3 neither provide the ``missing knowledge" and nor a good parser. This thesis does not provide an interpretable solution for this category but some preliminary results and analysis of a pure DL based approach. Nonetheless, the thesis shows beyond the world of pure DL based approaches, there are tools that can offer interpretable solutions for challenging tasks without using much resource and possibly with better accuracy.
Towards this goal, I work with a specific family of reading comprehension tasks, normally referred to as the Non-Extractive Reading Comprehension (NRC), where the given passage does not contain enough information and to correctly answer sophisticated reasoning and ``additional knowledge" is required. I have organized the NRC tasks into three categories. Here I present my solutions to the first two categories and some preliminary results on the third category.
Category 1 NRC tasks refer to the scenarios where the required ``additional knowledge" is missing but there exists a decent natural language parser. For these tasks, I learn the missing ``additional knowledge" with the help of the parser and a novel inductive logic programming. The learned knowledge is then used to answer new questions. Experiments on three NRC tasks show that this approach along with providing an interpretable solution achieves better or comparable accuracy to that of the state-of-the-art DL based approaches.
The category 2 NRC tasks refer to the alternate scenario where the ``additional knowledge" is available but no natural language parser works well for the sentences of the target domain. To deal with these tasks, I present a novel hybrid reasoning approach which combines symbolic and natural language inference (neural reasoning) and ultimately allows symbolic modules to reason over raw text without requiring any translation. Experiments on two NRC tasks shows its effectiveness.
The category 3 neither provide the ``missing knowledge" and nor a good parser. This thesis does not provide an interpretable solution for this category but some preliminary results and analysis of a pure DL based approach. Nonetheless, the thesis shows beyond the world of pure DL based approaches, there are tools that can offer interpretable solutions for challenging tasks without using much resource and possibly with better accuracy.
ContributorsMitra, Arindam (Author) / Baral, Chitta (Thesis advisor) / Lee, Joohyung (Committee member) / Yang, Yezhou (Committee member) / Devarakonda, Murthy (Committee member) / Arizona State University (Publisher)
Created2019