Summary of From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in Llms by Finetuning on Synthetic Data, By Zheyang Xiong et al.
From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data
by Zheyang Xiong, Vasilis Papageorgiou, Kangwook Lee, Dimitris Papailiopoulos
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to improve Large Language Models (LLMs) in processing long-context inputs, addressing limitations such as information retrieval and reasoning capabilities. The authors introduce a synthetic dataset comprising numerical key-value retrieval tasks and fine-tune popular models like GPT-3.5 Turbo and Mistral 7B on this dataset. Experimental results demonstrate significant improvements in LLMs’ performance on longer-context settings, with notable enhancements in information retrieval and reasoning capabilities. The study also evaluates the transfer of skills from synthetic to real task evaluations and compares finetuned LLMs’ performance on general benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better by teaching them how to process long sentences. Right now, these language models struggle when dealing with longer texts, which is a problem because we often need them to understand complex information. To fix this issue, the researchers created a special set of training exercises that help the models improve their ability to find specific pieces of information and make smart connections between different ideas. The results show that these exercises can significantly boost the models’ performance on longer texts, which is great news for using AI in applications like chatbots or virtual assistants. |
Keywords
* Artificial intelligence * Gpt
