Papers by Lorenzo Jaime Flores
On the Benefits of Fine-Grained Loss Truncation: A Case Study on Factuality in Summarization (2024.eacl-short)
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| Challenge: | Text summarization and simplification are among the most widely used applications of NLP, but they are prone to hallucination due to training on unaligned data. |
| Approach: | They propose a loss-truncation approach to modify the standard log loss to adaptively remove noisy examples during training to improve model performance. |
| Outcome: | The proposed approach yields a considerable number of hallucinated entities on various datasets. |
R2D2: Robust Data-to-Text with Replacement Detection (2022.emnlp-main)
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| Challenge: | Existing methods to mitigate unfaithful text generation are inadequate . data-to-text generation requires a structured input format . |
| Approach: | They propose a training framework that addresses unfaithful Data-to-Text generation by training a system as a generator and faithfulness discriminator with additional replacement detection and unlikelihood learning tasks. |
| Outcome: | The proposed training framework improves FeTaQA, LogicNLG, and ToTTo fidelity on D2T systems. |
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control (2023.eacl-main)
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| Challenge: | Existing models for LT2T generation focus on surface-level realizations without much logical inference. |
| Approach: | They propose a model that uses logic forms as fact verifiers and content planners to control LT2T generation. |
| Outcome: | Experimental results show that the proposed model addresses unfaithfulness and diversity issues simultaneously. |