58 citations · 140 across the 15 of their papers we have counts for
18 papers
REALISTA: Realistic Latent Adversarial Attacks that Elicit LLM Hallucinations
Buyun Liang, Jinqi Luo, Liangzu Peng +6
Large language models (LLMs) achieve strong performance across many tasks but remain vulnerable to hallucinations, making it important to systematically evaluate their reliability…
SECA: Semantically Equivalent and Coherent Attacks for Eliciting LLM Hallucinations
Buyun Liang, Liangzu Peng, Jinqi Luo +3
Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often exhibit hallucinations, raising serious concerns about their relia…
Conformal Information Pursuit for Interactively Guiding Large Language Models
Kwan Ho Ryan Chan, Yuyan Ge, Edgar Dobriban +2
A significant use case of instruction-finetuned Large Language Models (LLMs) is to solve question-answering tasks interactively. In this setting, an LLM agent is tasked with making…
IP-CRR: Information Pursuit for Interpretable Classification of Chest Radiology Reports
Yuyan Ge, Kwan Ho Ryan Chan, Pablo Messina +1
The development of AI-based methods to analyze radiology reports could lead to significant advances in medical diagnosis, from improving diagnostic accuracy to enhancing efficiency…
Concept Lancet: Image Editing with Compositional Representation Transplant
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan +3
Diffusion models are widely used for image editing tasks. Existing editing methods often design a representation manipulation procedure by curating an edit direction in the text em…
KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to Jailbreak LLMs
Buyun Liang, Kwan Ho Ryan Chan, Darshan Thaker +2
Jailbreak attacks exploit specific prompts to bypass LLM safeguards, causing the LLM to generate harmful, inappropriate, and misaligned content. Current jailbreaking methods rely h…
Do LLMs "know" internally when they follow instructions?
Juyeon Heo, Christina Heinze-Deml, Oussama Elachqar +5
Instruction-following is crucial for building AI agents with large language models (LLMs), as these models must adhere strictly to user-provided constraints and guidelines. However…
PaCE: Parsimonious Concept Engineering for Large Language Models
Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan +4
Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output includi…
Learning Interpretable Queries for Explainable Image Classification with Information Pursuit
Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan +3
Information Pursuit (IP) is an explainable prediction algorithm that greedily selects a sequence of interpretable queries about the data in order of information gain, updating its…
Contextual Knowledge Pursuit for Faithful Visual Synthesis
Jinqi Luo, Kwan Ho Ryan Chan, Dimitris Dimos +1
Modern text-to-vision generative models often hallucinate when the prompt describing the scene to be generated is underspecified. In large language models (LLMs), a prevalent strat…
Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions
Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele +1
Variational Information Pursuit (V-IP) is a framework for making interpretable predictions by design by sequentially selecting a short chain of task-relevant, user-defined and inte…
Variational Information Pursuit for Interpretable Predictions
Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin D. Haeffele +2
There is a growing interest in the machine learning community in developing predictive algorithms that are "interpretable by design". Towards this end, recent work proposes to make…
Unsupervised Manifold Linearizing and Clustering
Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan +3
We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine…
Efficient Maximal Coding Rate Reduction by Variational Forms
Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan +3
The principle of Maximal Coding Rate Reduction (MCR) has recently been proposed as a training objective for learning discriminative low-dimensional structures intrinsic to high…
Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction
Xili Dai, Shengbang Tong, Mingyang Li +8
This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a closed-loop transcription…
ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminati…
Deep Networks from the Principle of Rate Reduction
Kwan Ho Ryan Chan, Yaodong Yu, Chong You +3
This work attempts to interpret modern deep (convolutional) networks from the principles of rate reduction and (shift) invariant classification. We show that the basic iterative gr…
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction
Yaodong Yu, Kwan Ho Ryan Chan, Chong You +2
To learn intrinsic low-dimensional structures from high-dimensional data that most discriminate between classes, we propose the principle of Maximal Coding Rate Reduction ($\text{M…