Ensuring Safe and High-Quality Outputs: A Guideline Library Approach for Language Models

1 code implementation • 18 Mar 2024 • Yi Luo, Zhenghao Lin, Yuhao Zhang, Jiashuo Sun, Chen Lin, Chengjin Xu, Xiangdong Su, Yelong Shen, Jian Guo, Yeyun Gong

Subsequently, the retrieval model correlates new inputs with relevant guidelines, which guide LLMs in response generation to ensure safe and high-quality outputs, thereby aligning with human values.

Response Generation Retrieval

Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

3 code implementations • 15 Jul 2023 • Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M. Ni, Heung-Yeung Shum, Jian Guo

Although large language models (LLMs) have achieved significant success in various tasks, they often struggle with hallucination problems, especially in scenarios requiring deep and responsible reasoning.

Hallucination Knowledge Graphs +3

Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models

1 code implementation • 23 Apr 2023 • Jiashuo Sun, Yi Luo, Yeyun Gong, Chen Lin, Yelong Shen, Jian Guo, Nan Duan

By utilizing iterative bootstrapping, our approach enables LLMs to autonomously rectify errors, resulting in more precise and comprehensive reasoning chains.

APOLLO: An Optimized Training Approach for Long-form Numerical Reasoning

2 code implementations • 14 Dec 2022 • Jiashuo Sun, Hang Zhang, Chen Lin, Xiangdong Su, Yeyun Gong, Jian Guo

For the retriever, we adopt a number-aware negative sampling strategy to enable the retriever to be more discriminative on key numerical facts.

Conversational Question Answering