Publications (25)
Learning From Mistakes Makes LLM Better Reasoner
Shengnan An, Zexiong Ma, Zeqi Lin +3
Large language models (LLMs) recently exhibited remarkable reasoning capabilities on solving math problems. To further improve their reasoning capabilities, this work explores whet…
Iterative Utterance Segmentation for Neural Semantic Parsing
Yinuo Guo, Zeqi Lin, Jian-Guang Lou +1
Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To…
Skill-Based Few-Shot Selection for In-Context Learning
Shengnan An, Bo Zhou, Zeqi Lin +5
In-context learning is the paradigm that adapts large language models to downstream tasks by providing a few examples. Few-shot selection -- selecting appropriate examples for each…
When Language Model Meets Private Library
Daoguang Zan, Bei Chen, Zeqi Lin +3
With the rapid development of pre-training techniques, a number of language models have been pre-trained on large-scale code corpora and perform well in code generation. In this pa…
Make Your LLM Fully Utilize the Context
Shengnan An, Zexiong Ma, Zeqi Lin +2
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-m…
STAND-Guard: A Small Task-Adaptive Content Moderation Model
Minjia Wang, Pingping Lin, Siqi Cai +5
Content moderation, the process of reviewing and monitoring the safety of generated content, is important for development of welcoming online platforms and responsible large langua…