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Found 15 papers, 11 papers with code
Defending LLM Watermarking Against Spoofing Attacks with Contrastive Representation Learning
We identify two core challenges that make defending against spoofing difficult: (1) the need for watermarks to be both sensitive to semantic-distorting changes and insensitive to semantic-preserving edits, and (2) the contradiction between the need to detect global semantic shifts and the local, auto-regressive nature of most watermarking schemes.
ThinkPrune: Pruning Long Chain-of-Thought of LLMs via Reinforcement Learning
To fill this gap, ThinkPrune offers a simple solution that continuously trains the long-thinking LLMs via reinforcement learning (RL) with an added token limit, beyond which any unfinished thoughts and answers will be discarded, resulting in a zero reward.
SyncAnimation: A Real-Time End-to-End Framework for Audio-Driven Human Pose and Talking Head Animation
Generating talking avatar driven by audio remains a significant challenge.
Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning
Recent studies have identified one aggravating factor of LLM hallucinations as the knowledge inconsistency between pre-training and fine-tuning, where unfamiliar fine-tuning data mislead the LLM to fabricate plausible but wrong outputs.
Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective
This paper investigates Who's Harry Potter (WHP), a pioneering yet insufficiently understood method for LLM unlearning.
Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference
To achieve both goals, a mainstream class of LLM unlearning methods introduces an optimization framework with a combination of two objectives - maximizing the prediction loss on the forget documents while minimizing that on the retain documents, which suffers from two challenges, degenerated output and catastrophic forgetting.
Towards Efficient Information Fusion: Concentric Dual Fusion Attention Based Multiple Instance Learning for Whole Slide Images
In the realm of digital pathology, multi-magnification Multiple Instance Learning (multi-mag MIL) has proven effective in leveraging the hierarchical structure of Whole Slide Images (WSIs) to reduce information loss and redundant data.
Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion
Table question answering is a popular task that assesses a model's ability to understand and interact with structured data.
Correcting Diffusion Generation through Resampling
Despite diffusion models' superior capabilities in modeling complex distributions, there are still non-trivial distributional discrepancies between generated and ground-truth images, which has resulted in several notable problems in image generation, including missing object errors in text-to-image generation and low image quality.
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
We show that, when aleatoric uncertainty arises from ambiguity or under-specification in LLM inputs, this approach makes it possible to factor an (unclarified) LLM's predictions into separate aleatoric and epistemic terms, using a decomposition similar to the one employed by Bayesian neural networks.
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets.
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis
We then impose spatial attention control by combining the attention over the entire text description and that over the local description of the particular object in the corresponding pixel region of that object.
Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models
Based on this finding, we further propose a simple, light-weight image editing algorithm where the mixing weights of the two text embeddings are optimized for style matching and content preservation.
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection
Ideology is at the core of political science research.
Audrey: A Personalized Open-Domain Conversational Bot
Conversational Intelligence requires that a person engage on informational, personal and relational levels.
