3 papers
cs.CL2024
MATTER: Memory-Augmented Transformer Using Heterogeneous Knowledge Sources
Dongkyu Lee, Chandana Satya Prakash, Jack FitzGerald +1
Leveraging external knowledge is crucial for achieving high performance in knowledge-intensive tasks, such as question answering. The retrieve-and-read approach is widely adopted f…
cs.CL2024
Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding
Zheng Zhao, Emilio Monti, Jens Lehmann +1
Large language models (LLMs) tend to inadequately integrate input context during text generation, relying excessively on encoded prior knowledge in model parameters, potentially re…
cs.CL2024
REXEL: An End-to-end Model for Document-Level Relation Extraction and Entity Linking
Nacime Bouziani, Shubhi Tyagi, Joseph Fisher +2
Extracting structured information from unstructured text is critical for many downstream NLP applications and is traditionally achieved by closed information extraction (cIE). Howe…