Summary of Generative Representational Instruction Tuning, by Niklas Muennighoff et al.
Generative Representational Instruction Tuning
by Niklas Muennighoff, Hongjin Su, Liang Wang, Nan Yang, Furu Wei, Tao Yu, Amanpreet Singh, Douwe Kiela
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 research paper presents a novel approach to training large language models that can excel in both generative and embedding tasks. The authors introduce Generative Representational Instruction Tuning (GRIT), which distinguishes between the two tasks through instructions. This allows the model to be trained on both types of tasks simultaneously, achieving state-of-the-art performance on the Massive Text Embedding Benchmark (MTEB) and outperforming other models up to its size on various generative tasks. By scaling up further, the authors demonstrate that their GritLM 8x7B model can even surpass all open generative language models while still being among the best embedding models. The paper also shows that GRIT achieves performance comparable to training on only generative or embedding data, thus unifying both at no loss in performance. This has implications for applications like Retrieval-Augmented Generation (RAG), which can be sped up by over 60% for long documents without requiring separate retrieval and generation models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about making language models better! The researchers came up with a new way to train these models, called GRIT. It helps the model learn two important tasks: generating text (like writing a story) and embedding text (like matching words to their meanings). Right now, most models are good at one or the other, but not both. With GRIT, the model can do both really well! The new model, called GritLM 7B, is even better than others of its kind. If you want to use this model for things like writing stories or generating text, it’s a game-changer! |
Keywords
* Artificial intelligence * Embedding * Instruction tuning * Rag * Retrieval augmented generation
