Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models

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Open Access

1 January 2024

conference paper
Published by Association for Computational Linguistics (ACL)

p. 21456-21473
DOI:10.18653/v1/2024.emnlp-main.1196

Abstract

Jiaxin Zhang, Wendi Cui, Yiran Huang, Kamalika Das, Sricharan Kumar. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Keywords

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Version 1, 2024-10-12, ArXiv (Unconfirmed version)

This publication has 13 references indexed in Scilit:

Instruction Tuning for Large Language Models: A Survey
ACM Computing Surveys, 2026
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning
IEEE Transactions on Audio, Speech and Language Processing, 2025
Knowledge Editing for Large Language Models: A Survey
ACM Computing Surveys, 2024
A Survey of Knowledge Enhanced Pre-Trained Language Models
IEEE Transactions on Knowledge and Data Engineering, 2023
Large language models encode clinical knowledge
Nature, 2023
Prompt2Model: Generating Deployable Models from Natural Language Instructions
2023
Precise Zero-Shot Dense Retrieval without Relevance Labels
2023
KILM: Knowledge Injection into Encoder-Decoder Language Models
2023
A Survey of Knowledge-enhanced Text Generation
ACM Computing Surveys, 2022
Natural Questions: A Benchmark for Question Answering Research
Transactions of the Association for Computational Linguistics, 2019