{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/162730"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/162730","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Towards transparent representations: on internal structure and external world modeling in LLMs","abstract":"Large language models (LLMs) generalize far beyond their training distribution, enabling impressive downstream performance in domains vastly different from their pretraining distribution. In this thesis, we develop a data-centric view on machine learning. We suggest that the deep generalization of LLMs is best understood through studying the relationships between the four fundamental components of this data generalization: pretraining data, test-time inputs, model outputs, and internal structure. Of these, we present two full research studies characterizing test-time inputs and internal structure. Chapter 1 develops the data-centric view of machine learning, and outline the thesis. Chapter 2 presents Breakpoint, a method of generating difficult coding tasks for models at a large scale that attempts to disambiguate the factors that make problems at test-time difficult. Chapter 3 analyzes the structure of gradient-based jailbreaks in LLMs. We argue that even though GBJs are more out of distribution than even random text, they induce a low-rank, structured change in models. Finally, Chapter 4 discusses the recent rise of reasoning models and proposing some lines of future work in the data-centric view towards developing more robust understanding of LLMs.","abstract_html":"Large language models (LLMs) generalize far beyond their training distribution, enabling impressive downstream performance in domains vastly different from their pretraining distribution. In this thesis, we develop a data-centric view on machine learning. We suggest that the deep generalization of LLMs is best understood through studying the relationships between the four fundamental components of this data generalization: pretraining data, test-time inputs, model outputs, and internal structure. Of these, we present two full research studies characterizing test-time inputs and internal structure. Chapter 1 develops the data-centric view of machine learning, and outline the thesis. Chapter 2 presents Breakpoint, a method of generating difficult coding tasks for models at a large scale that attempts to disambiguate the factors that make problems at test-time difficult. Chapter 3 analyzes the structure of gradient-based jailbreaks in LLMs. We argue that even though GBJs are more out of distribution than even random text, they induce a low-rank, structured change in models. Finally, Chapter 4 discusses the recent rise of reasoning models and proposing some lines of future work in the data-centric view towards developing more robust understanding of LLMs.","abstract_has_math":false,"creators":["Hariharan, Kaivalya"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Finally, Chapter 4 discusses the recent rise of reasoning models and proposing some lines of future work in the data-centric view towards developing more robust understanding of LLMs."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Towards transparent representations: on internal structure and external world modeling in LLMs"]}]}],"canonical_facts":{"dc:contributor.advisor":["Andreas, Jacob"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Hariharan, Kaivalya"],"dc:date.accessioned":["2025-09-18T14:29:28Z"],"dc:date.available":["2025-09-18T14:29:28Z"],"dc:date.issued":["2025-05"],"dc:description.abstract":["Large language models (LLMs) generalize far beyond their training distribution, enabling impressive downstream performance in domains vastly different from their pretraining distribution. In this thesis, we develop a data-centric view on machine learning. We suggest that the deep generalization of LLMs is best understood through studying the relationships between the four fundamental components of this data generalization: pretraining data, test-time inputs, model outputs, and internal structure. Of these, we present two full research studies characterizing test-time inputs and internal structure. Chapter 1 develops the data-centric view of machine learning, and outline the thesis. Chapter 2 presents Breakpoint, a method of generating difficult coding tasks for models at a large scale that attempts to disambiguate the factors that make problems at test-time difficult. Chapter 3 analyzes the structure of gradient-based jailbreaks in LLMs. We argue that even though GBJs are more out of distribution than even random text, they induce a low-rank, structured change in models. 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