1 citations · 2 across the 4 of their papers we have counts for
4 papers
Collaborative decoding of critical tokens for boosting factuality of large language models
Lifeng Jin, Baolin Peng, Linfeng Song +3
The most common training pipeline for large language models includes pretraining, finetuning and aligning phases, with their respective resulting models, such as the pretrained mod…
Fine-Grained Self-Endorsement Improves Factuality and Reasoning
Ante Wang, Linfeng Song, Baolin Peng +5
This work studies improving large language model (LLM) generations at inference time by mitigating fact-conflicting hallucinations. Particularly, we propose a self-endorsement fram…
Inconsistent dialogue responses and how to recover from them
Mian Zhang, Lifeng Jin, Linfeng Song +2
One critical issue for chat systems is to stay consistent about preferences, opinions, beliefs and facts of itself, which has been shown a difficult problem. In this work, we study…
Friend-training: Learning from Models of Different but Related Tasks
Mian Zhang, Lifeng Jin, Linfeng Song +3
Current self-training methods such as standard self-training, co-training, tri-training, and others often focus on improving model performance on a single task, utilizing differenc…