22 citations · 29 across the 10 of their papers we have counts for
15 papers
ShieldCLIP: Selective Safety Alignment for Harmful Content Mitigation in Multimodal Foundation Models
Tobia Poppi, Silvia Cappelletti, Samuele Poppi +4
Multimodal encoders such as CLIP underlie many downstream systems, but their web-scale training data embed harmful associations that safety alignment must suppress without unnecess…
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions
Saleh Afroogh, Syed Ishtiaque Ahmed, Petra Ahrweiler +46
This study provides a cross-disciplinary examination of Explainable Artificial Intelligence (XAI) approaches-focusing on deep neural networks (DNNs) and large language models (LLMs…
Hallucination reduction with CASAL: Contrastive Activation Steering For Amortized Learning
Wannan, Yang, Xinchi Qiu +6
Large Language Models (LLMs) exhibit impressive capabilities but often hallucinate, confidently providing incorrect answers instead of admitting ignorance. Prior work has shown tha…
Measuring AI "Slop" in Text
Chantal Shaib, Tuhin Chakrabarty, Diego Garcia-Olano +1
AI "slop" is an increasingly popular term used to describe low-quality AI-generated text, but there is currently no agreed upon definition of this term nor a means to measure its o…
Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Output Prefilling
Silvia Cappelletti, Tobia Poppi, Samuele Poppi +5
Large Language Models (LLMs) are increasingly evaluated on multiple-choice question answering (MCQA) tasks using *first-token probability* (FTP), which selects the answer option wh…
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
Using Captum to Explain Generative Language Models
Vivek Miglani, Aobo Yang, Aram H. Markosyan +2
Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users' understanding of PyTorch m…
Error Discovery by Clustering Influence Embeddings
Fulton Wang, Julius Adebayo, Sarah Tan +2
We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery. We formalize coherence -- a requireme…
Intermediate Entity-based Sparse Interpretable Representation Learning
Diego Garcia-Olano, Yasumasa Onoe, Joydeep Ghosh +1
Interpretable entity representations (IERs) are sparse embeddings that are "human-readable" in that dimensions correspond to fine-grained entity types and values are predicted prob…
Improving and Diagnosing Knowledge-Based Visual Question Answering via Entity Enhanced Knowledge Injection
Diego Garcia-Olano, Yasumasa Onoe, Joydeep Ghosh
Knowledge-Based Visual Question Answering (KBVQA) is a bi-modal task requiring external world knowledge in order to correctly answer a text question and associated image. Recent si…
Biomedical Interpretable Entity Representations
Diego Garcia-Olano, Yasumasa Onoe, Ioana Baldini +3
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretabl…
Learning Dense Representations for Entity Retrieval
Daniel Gillick, Sayali Kulkarni, Larry Lansing +4
We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidat…
Explaining Deep Classification of Time-Series Data with Learned Prototypes
Alan H. Gee, Diego Garcia-Olano, Joydeep Ghosh +1
The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be confident applying them to high-risk decisions. In this paper, we l…
Applying Machine Learning Methods to Enhance the Distribution of Social Services in Mexico
Kris Sankaran, Diego Garcia-Olano, Mobin Javed +7
The Government of Mexico's social development agency, SEDESOL, is responsible for the administration of social services and has the mission of lifting Mexican families out of pover…