Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision
Instruction-following LLMs have recently allowed systems to discover hidden concepts from a collection of unstructured documents based on a natural language description of the purpose of the…
Re-evaluating Automatic LLM System Ranking for Alignment with Human Preference
Evaluating and ranking the capabilities of different LLMs is crucial for understanding their performance and alignment with human preferences. Due to the high cost and time-consuming nature of human…
Social-RAG: Retrieving from Group Interactions to Socially Ground AI Generation
AI agents are increasingly tasked with making proactive suggestions in online spaces where groups collaborate, yet risk being unhelpful or even annoying if they fail to match group preferences or…
Facets, Taxonomies, and Syntheses: Navigating Structured Representations in LLM-Assisted Literature Review
Comprehensive literature review requires synthesizing vast amounts of research — a labor intensive and cognitively demanding process. Most prior work focuses either on helping researchers deeply…
Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions
Multiple-choice question answering (MCQA) is a key competence of performant transformer language models that is tested by mainstream benchmarks. However, recent evidence shows that models can have…
DiscoveryBench: Towards Data-Driven Discovery with Large Language Models
Can the rapid advances in code generation, function calling, and data analysis using large language models (LLMs) help automate the search and verification of hypotheses purely from a set of…
Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data
The impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing vast amounts of…
Holistically Evaluating the Environmental Impact of Creating Language Models
As the performance of artificial intelligence systems has dramatically increased, so too has the environmental impact of creating these systems. While many model developers release estimates of the…
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data…
On Linear Representations and Pretraining Data Frequency in Language Models
Pretraining data has a direct impact on the behaviors and quality of language models (LMs), but we only understand the most basic principles of this relationship. While most work focuses on…