64 citations · 75 across the 7 of their papers we have counts for
7 papers
Satori-SWE: Evolutionary Test-Time Scaling for Sample-Efficient Software Engineering
Guangtao Zeng, Maohao Shen, Delin Chen +8
Language models (LMs) perform well on standardized coding benchmarks but struggle with real-world software engineering tasks such as resolving GitHub issues in SWE-Bench, especiall…
Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search
Maohao Shen, Guangtao Zeng, Zhenting Qi +7
Large language models (LLMs) have demonstrated remarkable reasoning capabilities across diverse domains. Recent studies have shown that increasing test-time computation enhances LL…
Sailor: Open Language Models for South-East Asia
Longxu Dou, Qian Liu, Guangtao Zeng +4
We present Sailor, a family of open language models ranging from 0.5B to 7B parameters, tailored for South-East Asian (SEA) languages. These models are continually pre-trained from…
TinyLlama: An Open-Source Small Language Model
Peiyuan Zhang, Guangtao Zeng, Tianduo Wang +1
We present TinyLlama, a compact 1.1B language model pretrained on around 1 trillion tokens for approximately 3 epochs. Building on the architecture and tokenizer of Llama 2, TinyLl…
One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning
Guangtao Zeng, Peiyuan Zhang, Wei Lu
Fine-tuning pre-trained language models for multiple tasks tends to be expensive in terms of storage. To mitigate this, parameter-efficient transfer learning (PETL) methods have be…
Unsupervised Non-transferable Text Classification
Guangtao Zeng, Wei Lu
Training a good deep learning model requires substantial data and computing resources, which makes the resulting neural model a valuable intellectual property. To prevent the neura…
On the Generation of Medical Dialogues for COVID-19
Wenmian Yang, Guangtao Zeng, Bowen Tan +9
Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of c…