15 citations · 23 across the 10 of their papers we have counts for
10 papers
When Do LLMs Admit Their Mistakes? Understanding The Role Of Model Belief In Retraction
Yuqing Yang, Robin Jia
We study the internal mechanisms that govern when LLMs choose to retract wrong answers, i.e., spontaneously and immediately acknowledge errors in their previously generated false a…
BeHonest: Benchmarking Honesty in Large Language Models
Steffi Chern, Zhulin Hu, Yuqing Yang +5
Previous works on Large Language Models (LLMs) have mainly focused on evaluating their helpfulness or harmlessness. However, honesty, another crucial alignment criterion, has recei…
Weak-to-Strong Reasoning
Yuqing Yang, Yan Ma, Pengfei Liu
When large language models (LLMs) exceed human-level capabilities, it becomes increasingly challenging to provide full-scale and accurate supervision for these models. Weak-to-stro…
OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI
Zhen Huang, Zengzhi Wang, Shijie Xia +25
The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…
Alignment for Honesty
Yuqing Yang, Ethan Chern, Xipeng Qiu +2
Recent research has made significant strides in aligning large language models (LLMs) with helpfulness and harmlessness. In this paper, we argue for the importance of alignment for…
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way
Kai Lv, Shuo Zhang, Tianle Gu +11
Large language models (LLMs) are increasingly pivotal in a wide range of natural language processing tasks. Access to pre-trained models, courtesy of the open-source community, has…
Plan, Verify and Switch: Integrated Reasoning with Diverse X-of-Thoughts
Tengxiao Liu, Qipeng Guo, Yuqing Yang +4
As large language models (LLMs) have shown effectiveness with different prompting methods, such as Chain of Thought, Program of Thought, we find that these methods have formed a gr…
Full Parameter Fine-tuning for Large Language Models with Limited Resources
Kai Lv, Yuqing Yang, Tengxiao Liu +3
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but demand massive GPU resources for training. Lowering the threshold for LLMs training would enc…
An AMR-based Link Prediction Approach for Document-level Event Argument Extraction
Yuqing Yang, Qipeng Guo, Xiangkun Hu +3
Recent works have introduced Abstract Meaning Representation (AMR) for Document-level Event Argument Extraction (Doc-level EAE), since AMR provides a useful interpretation of compl…
DORE: Document Ordered Relation Extraction based on Generative Framework
Qipeng Guo, Yuqing Yang, Hang Yan +2
In recent years, there is a surge of generation-based information extraction work, which allows a more direct use of pre-trained language models and efficiently captures output dep…