6 citations · 26 across the 12 of their papers we have counts for
12 papers
Is This Your Final Answer? Cross-Contextual Consistency as a Measure of LLM Credibility
Siyang Wu, Yibo Jiang, Bryon Aragam
Large language models (LLMs) are powerful black-box systems, making it difficult to discern whether their answers reflect stable internal beliefs or superficial pattern matching. W…
Do LLMs dream of elephants (when told not to)? Latent concept association and associative memory in transformers
Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar +1
Large Language Models (LLMs) have the capacity to store and recall facts. Through experimentation with open-source models, we observe that this ability to retrieve facts can be eas…
The Geometry of Categorical and Hierarchical Concepts in Large Language Models
Kiho Park, Yo Joong Choe, Yibo Jiang +1
The linear representation hypothesis is the informal idea that semantic concepts are encoded as linear directions in the representation spaces of large language models (LLMs). Prev…
On the Origins of Linear Representations in Large Language Models
Yibo Jiang, Goutham Rajendran, Pradeep Ravikumar +2
Recent works have argued that high-level semantic concepts are encoded "linearly" in the representation space of large language models. In this work, we study the origins of such l…
Direct Acquisition Optimization for Low-Budget Active Learning
Zhuokai Zhao, Yibo Jiang, Yuxin Chen
Active Learning (AL) has gained prominence in integrating data-intensive machine learning (ML) models into domains with limited labeled data. However, its effectiveness diminishes…
Uncovering Meanings of Embeddings via Partial Orthogonality
Yibo Jiang, Bryon Aragam, Victor Veitch
Machine learning tools often rely on embedding text as vectors of real numbers. In this paper, we study how the semantic structure of language is encoded in the algebraic structure…
Beyond Reverse KL: Generalizing Direct Preference Optimization with Diverse Divergence Constraints
Chaoqi Wang, Yibo Jiang, Chenghao Yang +2
The increasing capabilities of large language models (LLMs) raise opportunities for artificial general intelligence but concurrently amplify safety concerns, such as potential misu…
Learning nonparametric latent causal graphs with unknown interventions
Yibo Jiang, Bryon Aragam
We establish conditions under which latent causal graphs are nonparametrically identifiable and can be reconstructed from unknown interventions in the latent space. Our primary foc…
Invariant and Transportable Representations for Anti-Causal Domain Shifts
Yibo Jiang, Victor Veitch
Real-world classification problems must contend with domain shift, the (potential) mismatch between the domain where a model is deployed and the domain(s) where the training data w…
Associative Memory in Iterated Overparameterized Sigmoid Autoencoders
Yibo Jiang, Cengiz Pehlevan
Recent work showed that overparameterized autoencoders can be trained to implement associative memory via iterative maps, when the trained input-output Jacobian of the network has…
Meta-Learning to Cluster
Yibo Jiang, Nakul Verma
Clustering is one of the most fundamental and wide-spread techniques in exploratory data analysis. Yet, the basic approach to clustering has not really changed: a practitioner hand…
Model-Agnostic Meta-Learning using Runge-Kutta Methods
Daniel Jiwoong Im, Yibo Jiang, Nakul Verma
Meta-learning has emerged as an important framework for learning new tasks from just a few examples. The success of any meta-learning model depends on (i) its fast adaptation to ne…