67 citations · 78 across the 11 of their papers we have counts for
11 papers
SR: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning
Ruotian Ma, Peisong Wang, Cheng Liu +6
Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale…
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…
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…
Cross-Linguistic Syntactic Difference in Multilingual BERT: How Good is It and How Does It Affect Transfer?
Ningyu Xu, Tao Gui, Ruotian Ma +4
Multilingual BERT (mBERT) has demonstrated considerable cross-lingual syntactic ability, whereby it enables effective zero-shot cross-lingual transfer of syntactic knowledge. The t…
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…
SENT: Sentence-level Distant Relation Extraction via Negative Training
Ruotian Ma, Tao Gui, Linyang Li +3
Distant supervision for relation extraction provides uniform bag labels for each sentence inside the bag, while accurate sentence labels are important for downstream applications t…
BERT-ATTACK: Adversarial Attack Against BERT Using BERT
Linyang Li, Ruotian Ma, Qipeng Guo +2
Adversarial attacks for discrete data (such as texts) have been proved significantly more challenging than continuous data (such as images) since it is difficult to generate advers…
Simplify the Usage of Lexicon in Chinese NER
Ruotian Ma, Minlong Peng, Qi Zhang +1
Recently, many works have tried to augment the performance of Chinese named entity recognition (NER) using word lexicons. As a representative, Lattice-LSTM (Zhang and Yang, 2018) h…