Search Results for author:
Found 5 papers, 5 papers with code
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Large Language Models (LLMs) have shown enhanced capabilities of solving novel tasks by reasoning step-by-step known as Chain-of-Thought (CoT) reasoning; how can we instill the same capability of reasoning step-by-step on unseen tasks into LMs that possess less than <100B parameters?
CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification
To improve the correctness of the explanations, fine-tuning language models with explanation data is needed.
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks.
Mind the Gap! Injecting Commonsense Knowledge for Abstractive Dialogue Summarization
In this paper, we propose to leverage the unique characteristics of dialogues sharing commonsense knowledge across participants, to resolve the difficulties in summarizing them.
Ranked #1 on
Text Summarization
on DialogSum
Can Language Models perform Abductive Commonsense Reasoning?
Abductive Reasoning is a task of inferring the most plausible hypothesis given a set of observations.
