23 citations · 56 across the 18 of their papers we have counts for
23 papers
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…
Improving Interactive In-Context Learning from Natural Language Feedback
Martin Klissarov, Jonathan Cook, Diego Antognini +5
Adapting one's thought process based on corrective feedback is an essential ability in human learning, particularly in collaborative settings. In contrast, the current large langua…
Position: Introspective Experience from Conversational Environments as a Path to Better Learning
Claudiu Cristian Musat, Jackson Tolins, Diego Antognini +3
Current approaches to AI training treat reasoning as an emergent property of scale. We argue instead that robust reasoning emerges from linguistic self-reflection, itself internali…
InkFM: A Foundational Model for Full-Page Online Handwritten Note Understanding
Anastasiia Fadeeva, Vincent Coriou, Diego Antognini +2
Tablets and styluses are increasingly popular for taking notes. To optimize this experience and ensure a smooth and efficient workflow, it's important to develop methods for accura…
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…
Textual Explanations and Critiques in Recommendation Systems
Diego Antognini
Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited…
Assistive Recipe Editing through Critiquing
Diego Antognini, Shuyang Li, Boi Faltings +1
There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online…
Positive and Negative Critiquing for VAE-based Recommenders
Diego Antognini, Boi Faltings
Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the persp…
Multi-Step Critiquing User Interface for Recommender Systems
Diana Petrescu, Diego Antognini, Boi Faltings
Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow…
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…
Fast Multi-Step Critiquing for VAE-based Recommender Systems
Diego Antognini, Boi Faltings
Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to re…
Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts
Martin Milenkoski, Diego Antognini, Claudiu Musat
In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencod…
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…
Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context
Milena Filipovic, Blagoj Mitrevski, Diego Antognini +3
Recommender systems research tends to evaluate model performance offline and on randomly sampled targets, yet the same systems are later used to predict user behavior sequentially…
Addressing Fairness in Classification with a Model-Agnostic Multi-Objective Algorithm
Kirtan Padh, Diego Antognini, Emma Lejal Glaude +2
The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One…
Interacting with Explanations through Critiquing
Diego Antognini, Claudiu Musat, Boi Faltings
Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs t…
HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset
Diego Antognini, Boi Faltings
Today, recommender systems are an inevitable part of everyone's daily digital routine and are present on most internet platforms. State-of-the-art deep learning-based models requir…
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…
Multi-Gradient Descent for Multi-Objective Recommender Systems
Nikola Milojkovic, Diego Antognini, Giancarlo Bergamin +2
Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender sy…
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization
Diego Antognini, Boi Faltings
Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi…
Multi-Dimensional Explanation of Target Variables from Documents
Diego Antognini, Claudiu Musat, Boi Faltings
Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a docu…
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision
Athanasios Giannakopoulos, Diego Antognini, Claudiu Musat +2
Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or text spans, with the end goal of performing aspect-based sentiment analysis. The small amount of avail…