| {"paper_url": "https://huggingface.co/papers/2307.06962", "comment": "This is an automated message from the [Librarian Bot](https://huggingface.co/librarian-bots). I found the following papers similar to this paper. \n\nThe following papers were recommended by the Semantic Scholar API \n\n* [Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation](https://huggingface.co/papers/2404.05143) (2024)\n* [FlashBack:Efficient Retrieval-Augmented Language Modeling for Long Context Inference](https://huggingface.co/papers/2405.04065) (2024)\n* [Visually Guided Generative Text-Layout Pre-training for Document Intelligence](https://huggingface.co/papers/2403.16516) (2024)\n* [LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders](https://huggingface.co/papers/2404.05961) (2024)\n* [Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models](https://huggingface.co/papers/2404.04522) (2024)\n\n\n Please give a thumbs up to this comment if you found it helpful!\n\n If you want recommendations for any Paper on Hugging Face checkout [this](https://huggingface.co/spaces/librarian-bots/recommend_similar_papers) Space\n\n You can directly ask Librarian Bot for paper recommendations by tagging it in a comment: `@librarian-bot recommend`"} |