11 citations · 21 across the 11 of their papers we have counts for
9 papers · 1 filter
Learning to Learn from Language Feedback with Social Meta-Learning
Jonathan Cook, Diego Antognini, Martin Klissarov +2
Large language models (LLMs) often struggle to learn from corrective feedback within a conversational context. They are rarely proactive in soliciting this feedback, even when face…
Paraphrase and Solve: Exploring and Exploiting the Impact of Surface Form on Mathematical Reasoning in Large Language Models
Yue Zhou, Yada Zhu, Diego Antognini +2
This paper studies the relationship between the surface form of a mathematical problem and its solvability by large language models. We find that subtle alterations in the surface…
Interlock-Free Multi-Aspect Rationalization for Text Classification
Shuangqi Li, Diego Antognini, Boi Faltings
Explanation is important for text classification tasks. One prevalent type of explanation is rationales, which are text snippets of input text that suffice to yield the prediction…
Rationalization through Concepts
Diego Antognini, Boi Faltings
Automated predictions require explanations to be interpretable by humans. One type of explanation is a rationale, i.e., a selection of input features such as relevant text snippets…
An Enhanced MeanSum Method For Generating Hotel Multi-Review Summarizations
Saibo Geng, Diego Antognini
Multi-document summaritazion is the process of taking multiple texts as input and producing a short summary text based on the content of input texts. Up until recently, multi-docum…
GameWikiSum: a Novel Large Multi-Document Summarization Dataset
Diego Antognini, Boi Faltings
Today's research progress in the field of multi-document summarization is obstructed by the small number of available datasets. Since the acquisition of reference summaries is cost…