Search Results for author:
Found 39 papers, 24 papers with code
Retro-Search: Exploring Untaken Paths for Deeper and Efficient Reasoning
For weak-to-strong improvement, we retrospectively revise R1-671B's traces from the OpenThoughts dataset using R1-distill-32B as the Retro-Search-er, a model 20x smaller.
Geometry in Style: 3D Stylization via Surface Normal Deformation
We present Geometry in Style, a new method for identity-preserving mesh stylization.
Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models
Existing LLM reasoning methods have shown impressive capabilities across various tasks, such as solving math and coding problems.
SimpleToM: Exposing the Gap between Explicit ToM Inference and Implicit ToM Application in LLMs
"), and (c) judgment ("Mary paid for the chips.
HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions
AI agents are increasingly autonomous in their interactions with human users and tools, leading to increased interactional safety risks.
MeshUp: Multi-Target Mesh Deformation via Blended Score Distillation
We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each concept is expressed.
Probabilistic Vision-Language Representation for Weakly Supervised Temporal Action Localization
To address these problems, we propose a novel framework that aligns human action knowledge and VLP knowledge in a probabilistic embedding space.
Using LLMs to Investigate Correlations of Conversational Follow-up Queries with User Satisfaction
From qualitative analysis of 250 conversational turns from an in-lab user evaluation of Naver Cue:, a commercial conversational search engine, we propose a taxonomy of 18 users' follow-up query patterns from conversational search, comprising two major axes: (1) users' motivations behind continuing conversations (N = 7) and (2) actions of follow-up queries (N = 11).
Perceptions to Beliefs: Exploring Precursory Inferences for Theory of Mind in Large Language Models
Our evaluation of eight state-of-the-art LLMs reveals that the models generally perform well in perception inference while exhibiting limited capability in perception-to-belief inference (e. g., lack of inhibitory control).
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting
As the utilization of large language models (LLMs) has proliferated world-wide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures.
Learning Equi-angular Representations for Online Continual Learning
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e. g., single-epoch training).
Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs
Recent advances in large language models (LLM) have enabled richer social simulations, allowing for the study of various social phenomena.
Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMs
In this paper, we introduce a black-box prompt optimization method that uses an attacker LLM agent to uncover higher levels of memorization in a victim agent, compared to what is revealed by prompting the target model with the training data directly, which is the dominant approach of quantifying memorization in LLMs.
Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models
Effective interlocutors account for the uncertain goals, beliefs, and emotions of others.
Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory
The interactive use of large language models (LLMs) in AI assistants (at work, home, etc.)
FANToM: A Benchmark for Stress-testing Machine Theory of Mind in Interactions
Theory of mind (ToM) evaluations currently focus on testing models using passive narratives that inherently lack interactivity.
VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression
In content-based video retrieval (CBVR), dealing with large-scale collections, efficiency is as important as accuracy; thus, several video-level feature-based studies have actively been conducted.
Ranked #12 on
Video Retrieval
on FIVR-200K
SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization
Data scarcity has been a long standing issue in the field of open-domain social dialogue.
Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment
This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization.
Conservative Bayesian Model-Based Value Expansion for Offline Policy Optimization
Offline reinforcement learning (RL) addresses the problem of learning a performant policy from a fixed batch of data collected by following some behavior policy.
Towards Diverse Evaluation of Class Incremental Learning: A Representation Learning Perspective
Class incremental learning (CIL) algorithms aim to continually learn new object classes from incrementally arriving data while not forgetting past learned classes.
ProsocialDialog: A Prosocial Backbone for Conversational Agents
With this dataset, we introduce a dialogue safety detection module, Canary, capable of generating RoTs given conversational context, and a socially-informed dialogue agent, Prost.
Ranked #1 on
Dialogue Safety Prediction
on ProsocialDialog
Bridging the Gap between Classification and Localization for Weakly Supervised Object Localization
Weakly supervised object localization aims to find a target object region in a given image with only weak supervision, such as image-level labels.
Perception Prioritized Training of Diffusion Models
Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i. e., denoising score matching loss.
VISOLO: Grid-Based Space-Time Aggregation for Efficient Online Video Instance Segmentation
We also introduce cooperatively operating modules that aggregate information from available frames, in order to enrich the features for all subtasks in VIS.
Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
Empathy is a complex cognitive ability based on the reasoning of others' affective states.
How Robust are Fact Checking Systems on Colloquial Claims?
In this work, we ask: How robust are fact checking systems on claims in colloquial style?
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning
Online class-incremental continual learning (CL) studies the problem of learning new classes continually from an online non-stationary data stream, intending to adapt to new data while mitigating catastrophic forgetting.
Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring
However, the average gradient-based terms deployed in this method underestimates the contribution of the representations discovered by the model to its predictions.
Ada-SISE: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks
Explainable AI (XAI) is an active research area to interpret a neural network's decision by ensuring transparency and trust in the task-specified learned models.
Computational Efficiency
Explainable Artificial Intelligence (XAI)
Online Continual Learning in Image Classification: An Empirical Survey
To better understand the relative advantages of various approaches and the settings where they work best, this survey aims to (1) compare state-of-the-art methods such as MIR, iCARL, and GDumb and determine which works best at different experimental settings; (2) determine if the best class incremental methods are also competitive in domain incremental setting; (3) evaluate the performance of 7 simple but effective trick such as "review" trick and nearest class mean (NCM) classifier to assess their relative impact.
Deep neural network for solving differential equations motivated by Legendre-Galerkin approximation
Nonlinear differential equations are challenging to solve numerically and are important to understanding the dynamics of many physical systems.
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation.
Online Class-Incremental Continual Learning with Adversarial Shapley Value
As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption.
Batch-level Experience Replay with Review for Continual Learning
Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity.
Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness
Results on Dialogue NLI (Welleck et al., 2019) and PersonaChat (Zhang et al., 2018) dataset show that our approach reduces contradiction and improves consistency of existing dialogue models.
Learning Not to Learn: Training Deep Neural Networks with Biased Data
We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased.
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
We address the problem of abstractive summarization in two directions: proposing a novel dataset and a new model.
