18 citations · 97 across the 13 of their papers we have counts for
5 papers · 2 filters
Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models
Fangkai Jiao, Bosheng Ding, Tianze Luo +1
This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance. We explore how various…
Exploring Self-supervised Logic-enhanced Training for Large Language Models
Fangkai Jiao, Zhiyang Teng, Bosheng Ding +3
Existing efforts to improve logical reasoning ability of language models have predominantly relied on supervised fine-tuning, hindering generalization to new domains and/or tasks.…
Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources
Xingxuan Li, Ruochen Zhao, Yew Ken Chia +4
We present chain-of-knowledge (CoK), a novel framework that augments large language models (LLMs) by dynamically incorporating grounding information from heterogeneous sources. It…
Can ChatGPT-like Generative Models Guarantee Factual Accuracy? On the Mistakes of New Generation Search Engines
Ruochen Zhao, Xingxuan Li, Yew Ken Chia +2
Although large conversational AI models such as OpenAI's ChatGPT have demonstrated great potential, we question whether such models can guarantee factual accuracy. Recently, techno…
Retrieving Multimodal Information for Augmented Generation: A Survey
Ruochen Zhao, Hailin Chen, Weishi Wang +8
As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better intera…