6 citations · 9 across the 5 of their papers we have counts for
7 papers · 1 filter
Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
Jinliang Lu, Ziliang Pang, Min Xiao +3
The remarkable success of Large Language Models (LLMs) has ushered natural language processing (NLP) research into a new era. Despite their diverse capabilities, LLMs trained on di…
Diver: Large Language Model Decoding with Span-Level Mutual Information Verification
Jinliang Lu, Chen Wang, Jiajun Zhang
Large language models (LLMs) have shown impressive capabilities in adapting to various tasks when provided with task-specific instructions. However, LLMs using standard decoding st…
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions
Chong Li, Wen Yang, Jiajun Zhang +3
Large language models respond well in high-resource languages like English but struggle in low-resource languages. It may arise from the lack of high-quality instruction following…
Bridging the Gap between Different Vocabularies for LLM Ensemble
Yangyifan Xu, Jinliang Lu, Jiajun Zhang
Ensembling different large language models (LLMs) to unleash their complementary potential and harness their individual strengths is highly valuable. Nevertheless, vocabulary discr…
BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing
Chen Wang, Minpeng Liao, Zhongqiang Huang +5
The emergence of large language models (LLMs) has sparked significant interest in extending their remarkable language capabilities to speech. However, modality alignment between sp…
Instance-aware Prompt Learning for Language Understanding and Generation
Feihu Jin, Jinliang Lu, Jiajun Zhang +1
Recently, prompt learning has become a new paradigm to utilize pre-trained language models (PLMs) and achieves promising results in downstream tasks with a negligible increase of p…