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Hypothesis Testing Prompting Improves Deductive Reasoning in Large Language Models
Yitian Li, Jidong Tian, Hao He +1
Combining different forms of prompts with pre-trained large language models has yielded remarkable results on reasoning tasks (e.g. Chain-of-Thought prompting). However, along with…
Logical Negation Augmenting and Debiasing for Prompt-based Methods
Yitian Li, Jidong Tian, Hao He +1
Prompt-based methods have gained increasing attention on NLP and shown validity on many downstream tasks. Many works have focused on mining these methods' potential for knowledge e…
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'…
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…