Papers by Artidoro Pagnoni
EvEntS ReaLM: Event Reasoning of Entity States via Language Models (2022.emnlp-main)
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| Challenge: | Existing approaches to model event implications fail to reason about the world, despite their knowledge of physical attributes. |
| Approach: | They propose to use a model prompting technique to prompt models of event implications by targeting their understanding of physical attributes. |
| Outcome: | The proposed model prompting technique is especially useful for unseen attributes or when only limited data is available. |
Socratic Pretraining: Question-Driven Pretraining for Controllable Summarization (2023.acl-long)
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| Challenge: | Existing methods to control document controllable summarization lack abundant labeled data. |
| Approach: | They propose a question-driven, unsupervised pretraining objective to improve controllability in document controllable summarization tasks. |
| Outcome: | The proposed method outperforms pre-finetuning approaches on QMSum and SQuALITY. |
StructSum: Summarization via Structured Representations (2021.eacl-main)
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
| Challenge: | Abstractive summarization models overfit to training corpora, lack of transparency and layout bias . authors propose incorporating latent and explicit dependencies across sentences in source document . |
| Approach: | They propose a framework based on document-level structure induction to address layout bias and lack of transparency in abstractive summarization models. |
| Outcome: | The proposed framework improves coverage of content in the source documents and generates more abstractive summaries by generating more novel n-grams. |
Byte Latent Transformer: Patches Scale Better Than Tokens (2025.acl-long)
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Artidoro Pagnoni, Ramakanth Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason E Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srini Iyer
| Challenge: | Existing large language models (LLMs) are trained on bytes, except for tokenization, which groups bytes into a static set of tokens. |
| Approach: | They propose a new byte-level LLM architecture that encodes bytes into dynamically sized patches, which serve as the primary units of computation. |
| Outcome: | The proposed architecture matches tokenization-based models with improvements in inference efficiency and robustness. |
Definition Frames: Using Definitions for Hybrid Concept Representations (2020.coling-main)
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| Challenge: | a new hybrid representation is proposed that encodes semantic information extracted from definitions. |
| Approach: | They propose a matrix distributed representation extracted from definitions where each dimension is semantically interpretable. |
| Outcome: | The proposed representations have competitive performance with other distributional semantic approaches on word similarity tasks. |
Threat Scenarios and Best Practices to Detect Neural Fake News (2022.coling-1)
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| Challenge: | During the COVID-19 pandemic, inaccurate information made it hard for people to find reliable guidance when they needed it. |
| Approach: | They propose to use pretrained language models to generate fluent, original text . they argue that strong detectors should be released along with new generators . |
| Outcome: | The proposed system is prone to shortcut learning and should be released along with new generators. |
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (2021.naacl-main)
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| Challenge: | Modern summarization models generate fluent but often factually unreliable outputs. |
| Approach: | They propose to use human annotations to identify different categories of factual errors and benchmark factuality metrics to improve summarization evaluation. |
| Outcome: | The proposed method identifies the proportion of different categories of factual errors and benchmarks their human judgements as well as their specific strengths and weaknesses. |