International Conference on Learning Representations ↔ Large Language Models

Relations (1)

cross_type 2.00 — strongly supporting 3 facts

Large Language Models are the primary subject of research papers presented at the International Conference on Learning Representations, including studies on fine-tuning [1], low-rank adaptation [2], and data mixture strategies [3].

Facts (3)

Sources

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 3 facts

referenceThe paper 'SMT: fine-tuning large language models with sparse matrices' (The Thirteenth International Conference on Learning Representations) is cited in the survey 'A Survey on the Theory and Mechanism of Large Language Models' regarding fine-tuning.

referenceThe paper 'LoRA: low-rank adaptation of large language models' was published in the International Conference on Learning Representations.

referenceThe research paper 'Rethinking data mixture for large language models: a comprehensive survey and new perspectives' was published in The Thirteenth International Conference on Learning Representations and cited in section 4.2.2 of the survey.