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Articles by Heba Elfardy
[2601.23129] Evaluating the Utility of Grounding Documents with Reference-Free LLM-based Metrics
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SumREN: Summarizing reported speech about events in news
A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details.
Unified embeddings for multimodal retrieval via frozen LLMs
In this work, We present Unified Embeddings for Multimodal Retrieval (UNIMUR), a simple but effective approach that embeds multimodal inputs and retrieves visual and textual outputs via frozen Large Language Models (LLMs). Specifically, UNIMUR jointly retrieves multimodal outputs via unified multimodal embedding and applies dual alignment training to account for both visual and textual semantics.
Answer consolidation
Current question answering (QA) systems primarily consider the single-answer scenario, where each question is assumed to be paired with one correct answer. However, in many real-world QA applications, multiple answer scenarios arise where consolidating answers into a comprehensive and non-redundant set of answers is a more efficient user interface.
Answer consolidation: Formulation and benchmarking
At Amazon, we're working to be the Earth’s most customer-centric company. The goal of JP Last Mile Technology Team is to exceed the expectations of our customers by ensuring that their orders, no matter how large or small, are delivered as quickly, accurately, and cost effectively as possible. To meet this goal, Amazon is continually striving to innovate and provide best in class delivery experience through the introduction of pioneering new products and services in the last mile delivery space.
Enhancing multi-document summarization with cross-document graph-based information extraction
Information extraction (IE) and summarization are closely related, both tasked with presenting a subset of the information contained in a natural language text. However, while IE extracts structural representations, summarization aims to abstract the most salient information into a generated text summary – thus potentially encountering the technical limitations of current text generation methods (e.g., hallucination).
Amazon paper exposes biases in unreliable-news datasets
At the 2021 Conference of the European Association for Computational Linguistics (EACL), we received honorable mention in the best-long-paper category for our paper "Hidden biases in unreliable news detection datasets”, coauthored with Xiang Zhou (while he was an Amazon intern) and Mohit Bansal from the University of North California at Chapel Hill. In this paper, we studied datasets used by the research community for developing models to automatically identify unreliable news.
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