5 papers
G2LoRA: Gradient Orthogonal Low-Rank Adaptation Framework for Graph Continual Learning on Text-Attributed Graphs
Yuhan Wang, Yibo Ding, Yutong Ye +4
LLM-as-Aligner has emerged as a prevalent pre-training paradigm for Text-Attributed Graphs(TAGS), aligning graph and text modalities into a shared embedding space via CLIP-style co…
Synthetic Data from Cross-Domain Events for Large-Scale Recommendation Systems
Xiangyu Wang, Yawen He, Shivendra Pratap Singh +12
Large-scale recommendation systems operate across diverse domains, yet they face the challenges of data sparsity and noisy implicit feedback. Traditional approaches mitigate this v…
DistillLens: Symmetric Knowledge Distillation Through Logit Lens
Manish Dhakal, Uthman Jinadu, Anjila Budathoki +2
Standard Knowledge Distillation (KD) compresses Large Language Models (LLMs) by optimizing final outputs, yet it typically treats the teacher's intermediate layer's thought process…
Can a Unimodal Language Agent Provide Preferences to Tune a Multimodal Vision-Language Model?
Sazia Tabasum Mim, Jack Morris, Manish Dhakal +3
To explore a more scalable path for adding multimodal capabilities to existing LLMs, this paper addresses a fundamental question: Can a unimodal LLM, relying solely on text, reason…
GFT: Graph Feature Tuning for Efficient Point Cloud Analysis
Manish Dhakal, Venkat R. Dasari, Rajshekhar Sunderraman +1
Parameter-efficient fine-tuning (PEFT) significantly reduces computational and memory costs by updating only a small subset of the model's parameters, enabling faster adaptation to…