15 citations · 16 across the 15 of their papers we have counts for
15 papers
Few-Shot Demonstrations Elicit the Use of In-Context World Representations in LLMs
Kohsei Matsutani, Gouki Minegishi, Core Francisco Park +3
Large language models (LLMs), when acting as agents, are expected to take observed data in context, infer the latent state space underlying the world, and leverage it for downstrea…
Shared Circuits for Shared Grammar: Tracing Subject-Verb Agreement Across Languages
Isabella Gidi, Antonio Almudévar, Core Francisco Park +2
Multilingual large language models often generalize across languages, and prior work suggests that their internal mechanisms can overlap cross-lingually. It remains unclear, howeve…
CreativityNeuro: Steering Language Model Weights to Improve Divergent Thinking and Reduce Mode Collapse
Samuel Schapiro, Core Francisco Park, Felix Sosa +1
Divergent thinking is a crucial aspect of creativity, yet large language models (LLMs) tend to consistently generate similar responses to open-ended questions, in what has been ter…
Viral Proteins Reveal Geometry of Protein Language Models
Arthur Bigot, Harmon Bhasin, Core Francisco Park +2
Protein language models are trained on highly imbalanced datasets, raising the question of how they represent underrepresented biological sequences. Using viral proteins as a case…
A Systematic Study of Behavioral Cloning for Scientific Data Annotation
Ishaan Singh Chandok, Core Francisco Park
Scientific data annotation, such as tracking animals in video or proofreading neural reconstructions, remains bottlenecked by the "last mile" problem: even with strong automation,…
Mechanisms of Misgeneralization in Physical Sequence Modeling
Kento Nishi, Raphael Tang, Karun Kumar +2
Generative sequence models are often trained to plan motion in physical domains, from robotics to mechanical simulations. When constructing a dataset to train such a model, enginee…
Convergent World Representations and Divergent Tasks
Core Francisco Park
While neural representations are central to modern deep learning, the conditions governing their geometry and their roles in downstream adaptability remain poorly understood. We de…
In-Context Learning Strategies Emerge Rationally
Daniel Wurgaft, Ekdeep Singh Lubana, Core Francisco Park +3
Recent work analyzing in-context learning (ICL) has identified a broad set of strategies that describe model behavior in different experimental conditions. We aim to unify these fi…
Decomposing Elements of Problem Solving: What "Math" Does RL Teach?
Tian Qin, Core Francisco Park, Mujin Kwun +5
Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs, especially with reinforcement learning (RL) methods such as GRPO sho…
: System-2 Fine-tuning for Robust Integration of New Knowledge
Core Francisco Park, Zechen Zhang, Hidenori Tanaka
Humans and intelligent animals can internalize new information and accurately internalize their implications to perform downstream tasks. While large language models (LLMs) can ach…
ICLR: In-Context Learning of Representations
Core Francisco Park, Andrew Lee, Ekdeep Singh Lubana +5
Recent work has demonstrated that semantics specified by pretraining data influence how representations of different concepts are organized in a large language model (LLM). However…
Competition Dynamics Shape Algorithmic Phases of In-Context Learning
Core Francisco Park, Ekdeep Singh Lubana, Itamar Pres +1
In-Context Learning (ICL) has significantly expanded the general-purpose nature of large language models, allowing them to adapt to novel tasks using merely the inputted context. T…
Swing-by Dynamics in Concept Learning and Compositional Generalization
Yongyi Yang, Core Francisco Park, Ekdeep Singh Lubana +3
Prior work has shown that text-conditioned diffusion models can learn to identify and manipulate primitive concepts underlying a compositional data-generating process, enabling gen…
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space
Core Francisco Park, Maya Okawa, Andrew Lee +2
Modern generative models demonstrate impressive capabilities, likely stemming from an ability to identify and manipulate abstract concepts underlying their training data. However,…
Quantification of high dimensional non-Gaussianities and its implication to Fisher analysis in cosmology
Core Francisco Park, Erwan Allys, Francisco Villaescusa-Navarro +1
It is well known that the power spectrum is not able to fully characterize the statistical properties of non-Gaussian density fields. Recently, many different statistics have been…