Papers with adaptable language model

2 papers
Generation-driven Contrastive Self-training for Zero-shot Text Classification with Instruction-following LLM (2024.eacl-long)

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Challenge: a novel method to train a smaller model with LLMs for zero-shot text classification requires immense computational resources due to their substantial model size.
Approach: They propose a method which leverages the generative power of large language models to train a smaller model.
Outcome: The proposed method outperforms state-of-the-art methods when limited data is available.
Personalized Language Model for Query Auto-Completion (P18-2)

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Challenge: Query auto-completion (QAC) is a search engine feature that suggests completed queries as the user types . recent work suggests personalization of the recurrent layer to generate personalized completions.
Approach: They propose to use a recurrent neural network language model to generate personalized completions for search engines.
Outcome: The proposed model can generate personalized completions for users not seen during training.

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