15 citations · 59 across the 30 of their papers we have counts for
32 papers
RT-J: Large-Scale Pretraining of Relational Transformers for Context-Efficient Predictions
Rishabh Ranjan, Vignesh Kothapalli, Harshvardhan Agarwal +5
Foundation models for tables and relational databases are increasingly used for predictive tasks across various scientific and industrial domains. However, adapting existing models…
What Happens When the Model Eats the Stack? Rethinking the Research Agenda for Data Agents to Withstand the Bitter Lesson
Liana Patel, Siddharth Jha, Negar Arabzadeh +3
The bitter lesson poses an existential question for the data systems community, whereby large language models (LLMs) trained end-to-end are rapidly internalizing new capabilities t…
Beyond expert users: agents should help users construct preferences, not just elicit them
Irena Saracay, Ludwig Schmidt, Carlos Guestrin
Agents typically assume an expert user -- one with well-formed preferences about what they want -- and default to clarifying questions whenever the task is underspecified. We argue…
AI Assistance for Human Review of Default Judgments
Theodora Worledge, Othman Bensouda Koraichi, Daniel Bernal +4
Overwhelmed courts in the United States review millions of default judgments each year. Unfortunately, such manual reviews are time-consuming and prone to error. In an audit of 188…
Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning
Qinan Yu, Alexa Tartaglini, Peter Hase +2
Reinforcement Learning from Verifiable Rewards (RLVR) on chain-of-thought reasoning has become a standard part of language model post-training recipes. A common assumption is that…
Reinforcement Learning via Self-Distillation
Jonas Hübotter, Frederike Lübeck, Lejs Behric +8
Large language models are increasingly post-trained with reinforcement learning in verifiable domains such as code and math. Yet, current methods for reinforcement learning with ve…
ALMo: Interactive Aim-Limit-Defined, Multi-Objective System for Personalized High-Dose-Rate Brachytherapy Treatment Planning and Visualization for Cervical Cancer
Edward Chen, Natalie Dullerud, Pang Wei Koh +4
In complex clinical decision-making, clinicians must often track a variety of competing metrics defined by aim (ideal) and limit (strict) thresholds. Sifting through these high-dim…
Learning to Discover at Test Time
Mert Yuksekgonul, Daniel Koceja, Xinhao Li +8
How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We p…
Discovering Implicit Large Language Model Alignment Objectives
Edward Chen, Sanmi Koyejo, Carlos Guestrin
Large language model (LLM) alignment relies on complex reward signals that often obscure the specific behaviors being incentivized, creating critical risks of misalignment and rewa…
PluRel: Synthetic Data unlocks Scaling Laws for Relational Foundation Models
Vignesh Kothapalli, Rishabh Ranjan, Valter Hudovernik +4
Relational Foundation Models (RFMs) facilitate data-driven decision-making by learning from complex multi-table databases. However, the diverse relational databases needed to train…
End-to-End Test-Time Training for Long Context
Arnuv Tandon, Karan Dalal, Xinhao Li +11
We formulate long-context language modeling as a problem in continual learning rather than architecture design. Under this formulation, we only use a standard architecture -- a Tra…
Relational Transformer: Toward Zero-Shot Foundation Models for Relational Data
Rishabh Ranjan, Valter Hudovernik, Mark Znidar +7
Pretrained transformers readily adapt to new sequence modeling tasks via zero-shot prompting, but relational domains still lack architectures that transfer across datasets and task…
DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis
Liana Patel, Negar Arabzadeh, Harshit Gupta +4
The ability to research and synthesize knowledge is central to human expertise and progress. A new class of AI systems--designed for generative research synthesis--aims to automate…
Interactive Multi-Objective Probabilistic Preference Learning with Soft and Hard Bounds
Edward Chen, Sang T. Truong, Natalie Dullerud +2
High-stakes decision-making involves navigating multiple competing objectives with expensive evaluations. For instance, in brachytherapy, clinicians must balance maximizing tumor c…
Societal Impacts Research Requires Benchmarks for Creative Composition Tasks
Judy Hanwen Shen, Carlos Guestrin
Foundation models that are capable of automating cognitive tasks represent a pivotal technological shift, yet their societal implications remain unclear. These systems promise exci…
metaTextGrad: Automatically optimizing language model optimizers
Guowei Xu, Mert Yuksekgonul, Carlos Guestrin +1
Large language models (LLMs) are increasingly used in learning algorithms, evaluations, and optimization tasks. Recent studies have shown that using LLM-based optimizers to automat…
One-Minute Video Generation with Test-Time Training
Karan Dalal, Daniel Koceja, Gashon Hussein +12
Transformers today still struggle to generate one-minute videos because self-attention layers are inefficient for long context. Alternatives such as Mamba layers struggle with comp…
Obfuscated Activations Bypass LLM Latent-Space Defenses
Luke Bailey, Alex Serrano, Abhay Sheshadri +7
Recent latent-space monitoring techniques have shown promise as defenses against LLM attacks. These defenses act as scanners that seek to detect harmful activations before they lea…
Modeling Multi-Objective Tradeoffs with Monotonic Utility Functions
Edward Chen, Natalie Dullerud, Thomas Niedermayr +5
Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal (PO) solution which aligns with…
The Extractive-Abstractive Spectrum: Uncovering Verifiability Trade-offs in LLM Generations
Theodora Worledge, Tatsunori Hashimoto, Carlos Guestrin
Across all fields of academic study, experts cite their sources when sharing information. While large language models (LLMs) excel at synthesizing information, they do not provide…
Benchmarking Distributional Alignment of Large Language Models
Nicole Meister, Carlos Guestrin, Tatsunori Hashimoto
Language models (LMs) are increasingly used as simulacra for people, yet their ability to match the distribution of views of a specific demographic group and be \textit{distributio…
Model Equality Testing: Which Model Is This API Serving?
Irena Gao, Percy Liang, Carlos Guestrin
Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.g., Llama models are popularly accessed via Amazon Bedr…
Text2SQL is Not Enough: Unifying AI and Databases with TAG
Asim Biswal, Liana Patel, Siddarth Jha +5
AI systems that serve natural language questions over databases promise to unlock tremendous value. Such systems would allow users to leverage the powerful reasoning and knowledge…
Semantic Operators: A Declarative Model for Rich, AI-based Data Processing
Liana Patel, Siddharth Jha, Melissa Pan +4
The semantic capabilities of large language models (LLMs) have the potential to enable rich analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems eit…