8 papers
DRESS: Disentangled Representation-based Self-Supervised Meta-Learning for Diverse Tasks
Wei Cui, Tongzi Wu, Jesse C. Cresswell +2
Meta-learning represents a strong class of approaches for solving few-shot learning tasks. Nonetheless, recent research suggests that simply pre-training a generic encoder can pote…
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Brendan Leigh Ross, Noël Vouitsis, Atiyeh Ashari Ghomi +8
Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncertainty remains a critical open pr…
TFMLinker: Universal Link Predictor by Graph In-Context Learning with Tabular Foundation Models
Tianyin Liao, Chunyu Hu, Yicheng Sui +4
Link prediction is a fundamental task in graph machine learning with widespread applications such as recommendation systems, drug discovery, knowledge graphs, etc. In the foundatio…
Classifying and Addressing the Diversity of Errors in Retrieval-Augmented Generation Systems
Kin Kwan Leung, Mouloud Belbahri, Yi Sui +4
Retrieval-augmented generation (RAG) is a prevalent approach for building LLM-based question-answering systems that can take advantage of external knowledge databases. Due to the c…
Bridging External and Parametric Knowledge: Mitigating Hallucination of LLMs with Shared-Private Semantic Synergy in Dual-Stream Knowledge
Yi Sui, Chaozhuo Li, Chen Zhang +2
Retrieval-augmented generation (RAG) aims to mitigate the hallucination of Large Language Models (LLMs) by retrieving and incorporating relevant external knowledge into the generat…
Self-Supervised Representation Learning as Mutual Information Maximization
Akhlaqur Rahman Sabby, Yi Sui, Tongzi Wu +2
Self-supervised representation learning (SSRL) has demonstrated remarkable empirical success, yet its underlying principles remain insufficiently understood. While recent works att…