27 citations · 38 across the 10 of their papers we have counts for
10 papers
Effective Length Extrapolation via Dimension-Wise Positional Embeddings Manipulation
Yi Lu, Wanxu Zhao, Xin Zhou +9
Large Language Models (LLMs) often struggle to process and generate coherent context when the number of input tokens exceeds the pre-trained length. Recent advancements in long-con…
Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models
Xin Zhou, Yiwen Guo, Ruotian Ma +3
Aligning Large Language Models (LLMs) with human preferences is crucial for their deployment in real-world applications. Recent advancements in Self-Rewarding Language Models sugge…
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
Xin Zhou, Ping Nie, Yiwen Guo +7
Retrieval-Augmented Generation (RAG) significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. While existing research seeks to enhance…
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Yi Lu, Xin Zhou, Wei He +5
Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length…
Are Large Language Models Good Prompt Optimizers?
Ruotian Ma, Xiaolei Wang, Xin Zhou +5
LLM-based Automatic Prompt Optimization, which typically utilizes LLMs as Prompt Optimizers to self-reflect and refine prompts, has shown promising performance in recent studies. D…
Making Harmful Behaviors Unlearnable for Large Language Models
Xin Zhou, Yi Lu, Ruotian Ma +3
Large language models (LLMs) have shown great potential as general-purpose AI assistants in various domains. To meet the requirements of different applications, LLMs are often cust…
Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER
Ruotian Ma, Xuanting Chen, Lin Zhang +6
As the categories of named entities rapidly increase, the deployed NER models are required to keep updating toward recognizing more entity types, creating a demand for class-increm…
Searching for Optimal Subword Tokenization in Cross-domain NER
Ruotian Ma, Yiding Tan, Xin Zhou +6
Input distribution shift is one of the vital problems in unsupervised domain adaptation (UDA). The most popular UDA approaches focus on domain-invariant representation learning, tr…
Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models
Xin Zhou, Ruotian Ma, Tao Gui +3
Plug-and-play functionality allows deep learning models to adapt well to different tasks without requiring any parameters modified. Recently, prefix-tuning was shown to be a plug-a…
Template-free Prompt Tuning for Few-shot NER
Ruotian Ma, Xin Zhou, Tao Gui +4
Prompt-based methods have been successfully applied in sentence-level few-shot learning tasks, mostly owing to the sophisticated design of templates and label words. However, when…