90 citations · 145 across the 10 of their papers we have counts for
10 papers
PIN: A Knowledge-Intensive Dataset for Paired and Interleaved Multimodal Documents
Junjie Wang, Yuxiang Zhang, Minghao Liu +19
Recent advancements in large multimodal models (LMMs) have leveraged extensive multimodal datasets to enhance capabilities in complex knowledge-driven tasks. However, persistent ch…
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…
CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation
Daoguang Zan, Bei Chen, Dejian Yang +6
Code generation is a longstanding challenge, aiming to generate a code snippet based on a natural language description. Usually, expensive text-code paired data is essential for tr…
Input-Tuning: Adapting Unfamiliar Inputs to Frozen Pretrained Models
Shengnan An, Yifei Li, Zeqi Lin +6
Recently the prompt-tuning paradigm has attracted significant attention. By only tuning continuous prompts with a frozen pre-trained language model (PLM), prompt-tuning takes a ste…
LEMON: Language-Based Environment Manipulation via Execution-Guided Pre-training
Qi Shi, Qian Liu, Bei Chen +3
Language-based environment manipulation requires agents to manipulate the environment following natural language instructions, which is challenging due to the huge space of the env…
Learning Algebraic Recombination for Compositional Generalization
Chenyao Liu, Shengnan An, Zeqi Lin +6
Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks. Compositional generalization requires algebraic recombination, i.e., dynamica…