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Francis Ferraro

11 accepted papers

2025

ADAPTIVE IE: Investigating the Complementarity of Human-AI Collaboration to Adaptively Extract Information on-the-fly

Ishani Mondal, Michelle Yuan, Anandhavelu N, Aparna Garimella, Francis Ferraro, Andrew Blair-Stanek +2
COLING 2025main

Information extraction (IE) needs vary over time, where a flexible information extraction (IE) system can be useful. Despite this, existing IE systems are either fully supervised, requiring expensive human annotations, or fully unsupervised, extracting information that often do not cater to user’s n…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

CoRE: Condition-based Reasoning for Identifying Outcome Variance in Complex Events

Sai P Vallurupalli, Francis Ferraro
ACL 2025finding

Knowing which latent conditions lead to a particular outcome is useful for critically examining claims made about complex event outcomes. Identifying implied conditions and examining their influence on an outcome is challenging. We handle this by combining and augmenting annotations from two existin…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions

Seyedali Mohammadi, Bhaskara Hanuma Vedula, Hemank Lamba, Edward Raff, Ponnurangam Kumaraguru, Francis Ferraro +1
EMNLP 2025

Do LLMs genuinely incorporate external definitions, or do they primarily rely on their parametric knowledge? To address these questions, we conduct controlled experiments across multiple explanation benchmark datasets (general and domain-specific) and label definition conditions, including expert-cu

Cited by 0Source ↗PDF ↗Scholar ↗
2024

SAGA: A Participant-specific Examination of Story Alternatives and Goal Applicability for a Deeper Understanding of Complex Events

Sai Vallurupalli, Katrin Erk, Francis Ferraro
ACL 2024findings

Interpreting and assessing goal driven actions is vital to understanding and reasoning over complex events. It is important to be able to acquire the knowledge needed for this understanding, though doing so is challenging. We argue that such knowledge can be elicited through a participant achievemen…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2022

A General Framework for Auditing Differentially Private Machine Learning

Fred Lu, Joseph Munoz, Maya Fuchs, Tyler LeBlond, Elliott V. Zaresky-Williams, Edward Raff +2
NeurIPS 2022accept

We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or…

Cited by 41Source ↗PDF ↗Scholar ↗
2022

Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech

Gaoussou Youssouf Kebe, Luke E. Richards, Edward Raff, Francis Ferraro, Cynthia Matuszek
AAAI 2022technical

Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work, we demonstrate the feasibility of performing grounded language acquisition on paired…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗
2022

POQue: Asking Participant-specific Outcome Questions for a Deeper Understanding of Complex Events

Sai Vallurupalli, Sayontan Ghosh, Katrin Erk, Niranjan Balasubramanian, Francis Ferraro
EMNLP 2022main

Knowledge about outcomes is critical for complex event understanding but is hard to acquire.We show that by pre-identifying a participant in a complex event, crowdworkers are ableto (1) infer the collective impact of salient events that make up the situation, (2) annotate the volitional engagement o…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2021

A Spoken Language Dataset of Descriptions for Speech-Based Grounded Language Learning

Gaoussou Youssouf Kebe, Padraig Higgins, Patrick Jenkins, Kasra Darvish, Rishabh Sachdeva, Ryan Barron +5
NeurIPS 2021poster

Grounded language acquisition is a major area of research combining aspects of natural language processing, computer vision, and signal processing, compounded by domain issues requiring sample efficiency and other deployment constraints. In this work, we present a multimodal dataset of RGB+depth ob…

Cited by 24Source ↗Scholar ↗
2020

A Discrete Variational Recurrent Topic Model without the Reparametrization Trick

Mehdi Rezaee, Francis Ferraro
NeurIPS 2020poster

We show how to learn a neural topic model with discrete random variables---one that explicitly models each word's assigned topic---using neural variational inference that does not rely on stochastic backpropagation to handle the discrete variables. The model we utilize combines the expressive power…

Cited by 37Source ↗PDF ↗code ↗Scholar ↗