7 papers · 1 filter
Select-And-Extract: A Lightweight Plugin for Retrieval-Augmented Generation
Chenming Tang, Jiawei Han
Retrieval-augmented generation (RAG) for language model (LM) systems fundamentally has two failure modes: retrieval failure and reading failure. The former fails to recall the righ…
Aligning Language Models with Real-time Knowledge Editing
Chenming Tang, Yutong Yang, Kexue Wang +1
Knowledge editing aims to modify outdated knowledge in language models efficiently while retaining their original capabilities. Mainstream datasets for knowledge editing are predom…
CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark
Junzhao Zhang, Hsiu-Yuan Huang, Chenming Tang +2
Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limited cultural coverage, which h…
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions
Chenming Tang, Zhixiang Wang, Hao Sun +1
With the help of in-context learning (ICL), large language models (LLMs) have achieved impressive performance across various tasks. However, the function of descriptive instruction…
Lost in the Passage: Passage-level In-context Learning Does Not Necessarily Need a "Passage"
Hao Sun, Chenming Tang, Gengyang Li +1
By simply incorporating demonstrations into the context, in-context learning (ICL) enables large language models (LLMs) to yield awesome performance on many tasks. In this study, w…
SCOI: Syntax-augmented Coverage-based In-context Example Selection for Machine Translation
Chenming Tang, Zhixiang Wang, Yunfang Wu
In-context learning (ICL) greatly improves the performance of large language models (LLMs) on various down-stream tasks, where the improvement highly depends on the quality of demo…