20 citations · 25 across the 10 of their papers we have counts for
4 papers · 2 filters
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…
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…