Summary of Instruction Embedding: Latent Representations Of Instructions Towards Task Identification, by Yiwei Li et al.
Instruction Embedding: Latent Representations of Instructions Towards Task Identification
by Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Boyuan Pan, Heda Wang, Yao Hu, Kan Li
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A recent study, LIMA, reveals that Large Language Models’ (LLMs) ability to align with human-level performance relies on adapting instructions’ interaction style or format to solve various tasks. The key aspect of instructional data is the task it represents, rather than specific semantics and knowledge information. Instruction embeddings play a crucial role in instruction-related tasks like data selection and demonstrations retrieval, but these representations are derived from text embeddings, encompassing overall semantic information that influences task categories. This work introduces the concept of instruction embedding and constructs the Instruction Embedding Benchmark (IEB) for training and evaluation. A baseline Prompt-based Instruction Embedding (PIE) method is proposed to make representations more attentive to tasks. Experimental results on IEB with two designed tasks demonstrate PIE’s superior performance in accurately identifying task categories, while applications in four downstream tasks showcase its effectiveness and suitability for instruction-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are getting better at understanding human language, but they need the right instructions to do so. A new study shows that what matters most is what task you want the model to perform, not the specific details or knowledge it needs to solve that task. This is important because instructional data helps models adapt and learn new things. The researchers introduce a new concept called “instruction embeddings” which are like special codes that help models understand what tasks they need to do. They test this idea by creating a benchmark (a set of examples) for training and evaluating these instruction embeddings. Their results show that this approach works really well and can be used in many different areas. |
Keywords
» Artificial intelligence » Embedding » Prompt » Semantics
