8 citations · 20 across the 6 of their papers we have counts for
6 papers
Watermarking LLMs with Weight Quantization
Linyang Li, Botian Jiang, Pengyu Wang +3
Abuse of large language models reveals high risks as large language models are being deployed at an astonishing speed. It is important to protect the model weights to avoid malicio…
WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Models
Conghui He, Zhenjiang Jin, Chao Xu +6
The rise in popularity of ChatGPT and GPT-4 has significantly accelerated the development of large models, leading to the creation of numerous impressive large language models(LLMs…
Does Correction Remain A Problem For Large Language Models?
Xiaowu Zhang, Xiaotian Zhang, Cheng Yang +2
As large language models, such as GPT, continue to advance the capabilities of natural language processing (NLP), the question arises: does the problem of correction still persist?…
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search
Mozhi Zhang, Hang Yan, Yaqian Zhou +1
Few-shot Named Entity Recognition (NER) is a task aiming to identify named entities via limited annotated samples. Recently, prototypical networks have shown promising performance…
Unified Demonstration Retriever for In-Context Learning
Xiaonan Li, Kai Lv, Hang Yan +6
In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.…
Multi-way Particle Swarm Fusion
Chen Liu, Hang Yan, Pushmeet Kohli +1
This paper proposes a novel MAP inference framework for Markov Random Field (MRF) in parallel computing environments. The inference framework, dubbed Swarm Fusion, is a natural gen…