718 citations · 1k across the 44 of their papers we have counts for
12 papers · 1 filter
Exchange-of-Thought: Enhancing Large Language Model Capabilities through Cross-Model Communication
Zhangyue Yin, Qiushi Sun, Cheng Chang +4
Large Language Models (LLMs) have recently made significant strides in complex reasoning tasks through the Chain-of-Thought technique. Despite this progress, their reasoning is oft…
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…
Enhancing Uncertainty-Based Hallucination Detection with Stronger Focus
Tianhang Zhang, Lin Qiu, Qipeng Guo +6
Large Language Models (LLMs) have gained significant popularity for their impressive performance across diverse fields. However, LLMs are prone to hallucinate untruthful or nonsens…
StoryAnalogy: Deriving Story-level Analogies from Large Language Models to Unlock Analogical Understanding
Cheng Jiayang, Lin Qiu, Tsz Ho Chan +9
Analogy-making between narratives is crucial for human reasoning. In this paper, we evaluate the ability to identify and generate analogies by constructing a first-of-its-kind larg…
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…
AdaLomo: Low-memory Optimization with Adaptive Learning Rate
Kai Lv, Hang Yan, Qipeng Guo +2
Large language models have achieved remarkable success, but their extensive parameter size necessitates substantial memory for training, thereby setting a high threshold. While the…
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…
Do Large Language Models Know What They Don't Know?
Zhangyue Yin, Qiushi Sun, Qipeng Guo +3
Large language models (LLMs) have a wealth of knowledge that allows them to excel in various Natural Language Processing (NLP) tasks. Current research focuses on enhancing their pe…
Exploiting Abstract Meaning Representation for Open-Domain Question Answering
Cunxiang Wang, Zhikun Xu, Qipeng Guo +4
The Open-Domain Question Answering (ODQA) task involves retrieving and subsequently generating answers from fine-grained relevant passages within a database. Current systems levera…
All Roads Lead to Rome? Exploring the Invariance of Transformers' Representations
Yuxin Ren, Qipeng Guo, Zhijing Jin +4
Transformer models bring propelling advances in various NLP tasks, thus inducing lots of interpretability research on the learned representations of the models. However, we raise a…
Evaluating Open-QA Evaluation
Cunxiang Wang, Sirui Cheng, Qipeng Guo +7
This study focuses on the evaluation of the Open Question Answering (Open-QA) task, which can directly estimate the factuality of large language models (LLMs). Current automatic ev…