1 citations · 1 across the 6 of their papers we have counts for
6 papers
Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Models
Haoran Liao, Jidong Tian, Shaohua Hu +2
Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, th…
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…
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'…
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…