3 citations · 5 across the 8 of their papers we have counts for
8 papers
Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection
Caoyun Fan, Jidong Tian, Yitian Li +2
In-Context Learning (ICL) is an important paradigm for adapting Large Language Models (LLMs) to downstream tasks through a few demonstrations. Despite the great success of ICL, the…
Can Large Language Models Serve as Rational Players in Game Theory? A Systematic Analysis
Caoyun Fan, Jindou Chen, Yaohui Jin +1
Game theory, as an analytical tool, is frequently utilized to analyze human behavior in social science research. With the high alignment between the behavior of Large Language Mode…
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding
Caoyun Fan, Jidong Tian, Yitian Li +3
Chain-of-Thought (CoT) is a technique that guides Large Language Models (LLMs) to decompose complex tasks into multi-step reasoning through intermediate steps in natural language f…
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality
Caoyun Fan, Wenqing Chen, Jidong Tian +3
Multi-Label Text Classification (MLTC) aims to assign the most relevant labels to each given text. Existing methods demonstrate that label dependency can help to improve the model'…
Unlock the Potential of Counterfactually-Augmented Data in Out-Of-Distribution Generalization
Caoyun Fan, Wenqing Chen, Jidong Tian +3
Counterfactually-Augmented Data (CAD) -- minimal editing of sentences to flip the corresponding labels -- has the potential to improve the Out-Of-Distribution (OOD) generalization…
MaxGNR: A Dynamic Weight Strategy via Maximizing Gradient-to-Noise Ratio for Multi-Task Learning
Caoyun Fan, Wenqing Chen, Jidong Tian +3
When modeling related tasks in computer vision, Multi-Task Learning (MTL) can outperform Single-Task Learning (STL) due to its ability to capture intrinsic relatedness among tasks.…
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough
Caoyun Fan, Wenqing Chen, Jidong Tian +3
Counterfactually-Augmented Data (CAD) has the potential to improve language models' Out-Of-Distribution (OOD) generalization capability, as CAD induces language models to exploit c…
Dependent Multi-Task Learning with Causal Intervention for Image Captioning
Wenqing Chen, Jidong Tian, Caoyun Fan +2
Recent work for image captioning mainly followed an extract-then-generate paradigm, pre-extracting a sequence of object-based features and then formulating image captioning as a si…