6 papers
Media Content Atlas: A Pipeline to Explore and Investigate Multidimensional Media Space using Multimodal LLMs
Merve Cerit, Eric Zelikman, Mu-Jung Cho +4
As digital media use continues to evolve and influence various aspects of life, developing flexible and scalable tools to study complex media experiences is essential. This study i…
Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation
Eric Zelikman, Eliana Lorch, Lester Mackey +1
Several recent advances in AI systems solve problems by providing a "scaffolding" program that structures multiple calls to language models (LMs) to generate better outputs. A scaf…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9
Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…
Hypothesis Search: Inductive Reasoning with Language Models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia +3
Inductive reasoning is a core problem-solving capacity: humans can identify underlying principles from a few examples, which robustly generalize to novel scenarios. Recent work eva…
Self-Supervised Alignment with Mutual Information: Learning to Follow Principles without Preference Labels
Jan-Philipp Fränken, Eric Zelikman, Rafael Rafailov +3
When prompting a language model (LM), users often expect the model to adhere to a set of behavioral principles across diverse tasks, such as producing insightful content while avoi…