6 citations · 10 across the 10 of their papers we have counts for
11 papers
Testing the Assumptions of Active Learning for Translation Tasks with Few Samples
Lorenzo Jaime Yu Flores, Cesare Spinoso di-Piano, Ori Ernst +2
Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which is useful when only a limited number of…
Confident in a Confidence Score: Investigating the Sensitivity of Confidence Scores to Supervised Fine-Tuning
Lorenzo Jaime Yu Flores, Cesare Spinoso di-Piano, Cesare Spinoso Di-Piano +1
Uncertainty quantification techniques measure confidence in language model outputs to support critical applications like hallucination detection and selective prediction. While pri…
Towards Reliable Multi-Agent Systems for Marketing Applications via Reflection, Memory, and Planning
Lorenzo Jaime Yu Flores, Junyi Shen, Goodman Gu
Recent advances in large language models (LLMs) enabled the development of AI agents that can plan and interact with tools to complete complex tasks. However, literature on their r…
Improving the Calibration of Confidence Scores in Text Generation Using the Output Distribution's Characteristics
Lorenzo Jaime Yu Flores, Ori Ernst, Jackie Chi Kit Cheung
Well-calibrated model confidence scores can improve the usefulness of text generation models. For example, users can be prompted to review predictions with low confidence scores, t…
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization
Lorenzo Jaime Yu Flores, Arman Cohan
Text summarization and simplification are among the most widely used applications of AI. However, models developed for such tasks are often prone to hallucination, which can result…
Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding
Lorenzo Jaime Yu Flores, Heyuan Huang, Kejian Shi +2
Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often do…
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control
Yilun Zhao, Zhenting Qi, Linyong Nan +2
Logical Table-to-Text (LT2T) generation is tasked with generating logically faithful sentences from tables. There currently exists two challenges in the field: 1) Faithfulness: how…
Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino
Lorenzo Jaime Yu Flores, Dragomir Radev
With 84.75 million Filipinos online, the ability for models to process online text is crucial for developing Filipino NLP applications. To this end, spelling correction is a crucia…
R2D2: Robust Data-to-Text with Replacement Detection
Linyong Nan, Lorenzo Jaime Yu Flores, Yilun Zhao +4
Unfaithful text generation is a common problem for text generation systems. In the case of Data-to-Text (D2T) systems, the factuality of the generated text is particularly crucial…
An Adversarial Benchmark for Fake News Detection Models
Lorenzo Jaime Yu Flores, Yiding Hao
With the proliferation of online misinformation, fake news detection has gained importance in the artificial intelligence community. In this paper, we propose an adversarial benchm…
Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information
Chiara Ledesma, Oshean Lee Garonita, Lorenzo Jaime Flores +2
Access to accurate, granular, and up-to-date poverty data is essential for humanitarian organizations to identify vulnerable areas for poverty alleviation efforts. Recent works hav…