34 citations · 152 across the 20 of their papers we have counts for
20 papers
GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning
Ornith Team, Xiaoya Li, Guoyin Wang +3
Competitive programming remains one of the last few human strongholds in coding against AI. The best AI system to date still underperforms the best humans competitive programming:…
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, Bin Xiao, Chao Wang +86
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…
CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning
Songqiao Su, Xiaoya Li, Albert Wang +3
In this paper, we propose CUDA-L2, a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multi…
CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search
Xiaoya Li, Albert Wang, Guoyin Wang +2
Approximate nearest-neighbor search (ANNS) algorithms have become increasingly critical for recent AI applications, particularly in retrieval-augmented generation (RAG) and agent-b…
CUDA-L1: Improving CUDA Optimization via Contrastive Reinforcement Learning
Xiaoya Li, Albert Wang, Guoyin Wang +2
The exponential growth in demand for GPU computing resources has created an urgent need for automated CUDA optimization strategies. While recent advances in LLMs show promise for c…
FaceID-6M: A Large-Scale, Open-Source FaceID Customization Dataset
Shuhe Wang, Xiaoya Li, Jiwei Li +8
Due to the data-driven nature of current face identity (FaceID) customization methods, all state-of-the-art models rely on large-scale datasets containing millions of high-quality…
Turn That Frown Upside Down: FaceID Customization via Cross-Training Data
Shuhe Wang, Xiaoya Li, Xiaofei Sun +4
Existing face identity (FaceID) customization methods perform well but are limited to generating identical faces as the input, while in real-world applications, users often desire…
Reinforcement Learning Enhanced LLMs: A Survey
Shuhe Wang, Shengyu Zhang, Jie Zhang +7
Reinforcement learning (RL) enhanced large language models (LLMs), particularly exemplified by DeepSeek-R1, have exhibited outstanding performance. Despite the effectiveness in imp…
Sim-GPT: Text Similarity via GPT Annotated Data
Shuhe Wang, Beiming Cao, Shengyu Zhang +5
Due to the lack of a large collection of high-quality labeled sentence pairs with textual similarity scores, existing approaches for Semantic Textual Similarity (STS) mostly rely o…
Sentiment Analysis through LLM Negotiations
Xiaofei Sun, Xiaoya Li, Shengyu Zhang +5
A standard paradigm for sentiment analysis is to rely on a singular LLM and makes the decision in a single round under the framework of in-context learning. This framework suffers…
Faster Nearest Neighbor Machine Translation
Shuhe Wang, Jiwei Li, Yuxian Meng +5
NN based neural machine translation (NN-MT) has achieved state-of-the-art results in a variety of MT tasks. One significant shortcoming of NN-MT lies in its inefficiency i…
Triggerless Backdoor Attack for NLP Tasks with Clean Labels
Leilei Gan, Jiwei Li, Tianwei Zhang +6
Backdoor attacks pose a new threat to NLP models. A standard strategy to construct poisoned data in backdoor attacks is to insert triggers (e.g., rare words) into selected sentence…
Interpreting Deep Learning Models in Natural Language Processing: A Review
Xiaofei Sun, Diyi Yang, Xiaoya Li +6
Neural network models have achieved state-of-the-art performances in a wide range of natural language processing (NLP) tasks. However, a long-standing criticism against neural netw…
GNN-LM: Language Modeling based on Global Contexts via GNN
Yuxian Meng, Shi Zong, Xiaoya Li +4
Inspired by the notion that ``{\it to copy is easier than to memorize}``, in this work, we introduce GNN-LM, which extends the vanilla neural language model (LM) by allowing to ref…
OpenViDial 2.0: A Larger-Scale, Open-Domain Dialogue Generation Dataset with Visual Contexts
Shuhe Wang, Yuxian Meng, Xiaoya Li +3
In order to better simulate the real human conversation process, models need to generate dialogue utterances based on not only preceding textual contexts but also visual contexts.…
Folden: -Fold Ensemble for Out-Of-Distribution Detection
Xiaoya Li, Jiwei Li, Xiaofei Sun +5
Out-of-Distribution (OOD) detection is an important problem in natural language processing (NLP). In this work, we propose a simple yet effective framework Folden, which mimics…
Defending Against Backdoor Attacks in Natural Language Generation
Xiaofei Sun, Xiaoya Li, Yuxian Meng +4
The frustratingly fragile nature of neural network models make current natural language generation (NLG) systems prone to backdoor attacks and generate malicious sequences that cou…
Fast Nearest Neighbor Machine Translation
Yuxian Meng, Xiaoya Li, Xiayu Zheng +4
Though nearest neighbor Machine Translation (NN-MT) \citep{khandelwal2020nearest} has proved to introduce significant performance boosts over standard neural MT systems, it is p…
Improving Robustness and Generality of NLP Models Using Disentangled Representations
Jiawei Wu, Xiaoya Li, Xiang Ao +3
Supervised neural networks, which first map an input to a single representation , and then map to the output label , have achieved remarkable success in a wide range…
Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions
Xiaoya Li, Mingxin Zhou, Jiawei Wu +3
At the time of writing, the ongoing pandemic of coronavirus disease (COVID-19) has caused severe impacts on society, economy and people's daily lives. People constantly express the…