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Found 5 papers, 1 papers with code
Is Factuality Decoding a Free Lunch for LLMs? Evaluation on Knowledge Editing Benchmark
The rapid development of large language models (LLMs) enables them to convey factual knowledge in a more human-like fashion.
Graph Descriptive Order Improves Reasoning with Large Language Model
In this work, we reveal the impact of the order of graph description on LLMs' graph reasoning performance, which significantly affects LLMs' reasoning abilities.
LPNL: Scalable Link Prediction with Large Language Models
This work focuses on the link prediction task and introduces $\textbf{LPNL}$ (Link Prediction via Natural Language), a framework based on large language models designed for scalable link prediction on large-scale heterogeneous graphs.
SLANG: New Concept Comprehension of Large Language Models
The dynamic nature of language, particularly evident in the realm of slang and memes on the Internet, poses serious challenges to the adaptability of large language models (LLMs).
Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation
To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs.
