Papers by Enrique Alfonseca
SKILL: Structured Knowledge Infusion for Large Language Models (2022.naacl-main)
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| Challenge: | Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. |
| Approach: | They propose a method to infuse structured knowledge into large language models by directly training T5 models on factual triples of knowledge graphs (KGs). |
| Outcome: | The proposed method outperforms baseline models on FreebaseQA and WikiHop, as well as the Wikidata-answerable subset of TriviaQA and NaturalQuestions. |
SamToNe: Improving Contrastive Loss for Dual Encoder Retrieval Models with Same Tower Negatives (2023.findings-acl)
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| Challenge: | Dual encoders have been used for retrieval tasks and representation learning with good results. |
| Approach: | They propose an improved contrastive learning objective by adding queries or documents from the same encoder towers to the negatives. |
| Outcome: | The proposed model improves retrieval quality for both symmetric and asymmetric dual encoders by adding queries or documents from the same encoder towers to the negatives. |
Exploring Dual Encoder Architectures for Question Answering (2022.emnlp-main)
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| Challenge: | Dual encoders have been used for question-answering and information retrieval tasks with good results. |
| Approach: | They propose to use two different versions of dual encoders for QA retrieval tasks . they propose to share parameters in projection layers between two encoder towers . |
| Outcome: | The proposed architectures outperform SDE and ADE on QA retrieval tasks. |