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Photonic Fabric Platform for AI Accelerators

Achieving Robust Channel Estimation Neural Networks by Designed Training Data

Can Mental Imagery Improve the Thinking Capabilities of AI Systems?

Characterizing State Space Model (SSM) and SSM-Transformer Hybrid Language Model Performance with Long Context Length

PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training

Robust 3D-Masked Part-level Editing in 3D Gaussian Splatting with Regularized Score Distillation Sampling

A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n

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Resolving Token-Space Gradient Conflicts: Token Space Manipulation for Transformer-Based Multi-Task Learning

Fast Bilateral Teleoperation and Imitation Learning Using Sensorless Force Control via Accurate Dynamics Model

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Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning

Learning Software Bug Reports: A Systematic Literature Review

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Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecasting

TopoStreamer: Temporal Lane Segment Topology Reasoning in Autonomous Driving

“Before, I Asked My Mom, Now I Ask ChatGPT”: Visual Privacy Management with Generative AI for Blind and Low-Vision People

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FedWSQ: Efficient Federated Learning with Weight Standardization and Distribution-Aware Non-Uniform Quantization

Plan for Speed: Dilated Scheduling for Masked Diffusion Language Models

Bridging the Digital Divide: Small Language Models as a Pathway for Physics and Photonics Education in Underdeveloped Regions

DaMO: A Data-Efficient Multimodal Orchestrator for Temporal Reasoning with Video LLMs

Dynamic Context Tuning for Retrieval-Augmented Generation: Enhancing Multi-Turn Planning and Tool Adaptation

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation

Draft-based Approximate Inference for LLMs

Label-semantics Aware Generative Approach for Domain-Agnostic Multilabel Classification

SemiOccam: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels

Adversarial bandit optimization for approximately linear functions

Know Or Not: a library for evaluating out-of-knowledge base robustness

Leveraging Vision-Language Models for Visual Grounding and Analysis of Automotive UI

DualReal: Adaptive Joint Training for Lossless Identity-Motion Fusion in Video Customization

CoordField: Coordination Field for Agentic UAV Task Allocation In Low-altitude Urban Scenarios

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AnyTSR: Any-Scale Thermal Super-Resolution for UAV

Enhanced Pruning Strategy for Multi-Component Neural Architectures Using Component-Aware Graph Analysis

Executable Functional Abstractions: Inferring Generative Programs for Advanced Math Problems

Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach

APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay

The Dual-Route Model of Induction

Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

SWI: Speaking with Intent in Large Language Models

A Study of LLMs' Preferences for Libraries and Programming Languages

TruthLens: Explainable DeepFake Detection for Face Manipulated and Fully Synthetic Data

Sampling Decisions

Federated Continuous Instruction Tuning

Fine-Tuning Diffusion Generative Models via Rich Preference Optimization

BriLLM: Brain-inspired Large Language Model

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RealGeneral: Unifying Visual Generation via Temporal In-Context Learning with Video Models

Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

PLADIS: Pushing the Limits of Attention in Diffusion Models at Inference Time by Leveraging Sparsity

DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability

Symbolic Mixture-of-Experts: Adaptive Skill-based Routing for Heterogeneous Reasoning

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An Overall Real-Time Mechanism for Classification and Quality Evaluation of Rice

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A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios

PEMF-VTO: Point-Enhanced Video Virtual Try-on via Mask-free Paradigm

Understanding the Design Decisions of Retrieval-Augmented Generation Systems

DOGR: Towards Versatile Visual Document Grounding and Referring

Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking

DualSwinUnet++: An Enhanced Swin-Unet Architecture With Dual Decoders For PTMC Segmentation

PerspectiveNet: Multi-View Perception for Dynamic Scene Understanding

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ASMA: An Adaptive Safety Margin Algorithm for Vision-Language Drone Navigation via Scene-Aware Control Barrier Functions

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Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation

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Proficient Graph Neural Network Design by Accumulating Knowledge on Large Language Models

Stimulating Imagination: Towards General-purpose "Something Something Placement"

Why Does New Knowledge Create Messy Ripple Effects in LLMs?

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Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles

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Why Does New Knowledge Create Messy Ripple Effects in LLMs?

Created by

Haebom

Author

Jiaxin Qin, Zixuan Zhang, Manling Li, Pengfei Yu, Heng Ji

Outline

This paper focuses on the study of keeping the knowledge of language models (LMs) accurate and up-to-date through post-training knowledge editing (KE). In particular, we address the problem of ensuring that LMs correctly answer logically related knowledge after knowledge editing, that is, properly handling ripple effects. We analyze why existing KE methods still generate confusing ripple effects and propose a metric called GradSim, which measures the cosine similarity between the gradient of the original facts and the related knowledge. We observe a strong positive correlation between ripple effect performance and GradSim across a variety of LMs, KE methods, and evaluation metrics, and show that three counterintuitive failure cases, such as negation, excessive ripple effects, and multilingualism, are associated with low GradSim. In conclusion, we verify that GradSim is an effective metric for indicating when knowledge ripples in LMs.

Takeaways, Limitations

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Takeaways: GradSim suggests that LM is a useful metric for predicting and analyzing spillover effects after knowledge editing. This can contribute to improving the performance of KE methods and solving spillover problems.

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Limitations: GradSim suggests correlations, but does not fully prove causality. In addition, additional analysis of other types of failure cases beyond the three presented is needed. Further research is needed on the computational cost and generalization performance of GradSim.

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