Summary of Codeclm: Aligning Language Models with Tailored Synthetic Data, by Zifeng Wang et al.
CodecLM: Aligning Language Models with Tailored Synthetic Data
by Zifeng Wang, Chun-Liang Li, Vincent Perot, Long T. Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
First submitted to arxiv on: 8 Apr 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 introduces a new framework, CodecLM, for generating high-quality synthetic data to align large language models (LLMs) with specific task instructions. The authors focus on reducing the labor and time cost of collecting or annotating data by humans, instead using LLMs to generate instruction-aligned synthetic data. They propose a general approach that adaptively generates tailored instructions based on the target instruction distribution and LLM used. The method involves encoding seed instructions into metadata, which is then decoded to create tailored instructions. Additionally, the authors introduce Self-Rubrics and Contrastive Filtering techniques during decoding to ensure data efficiency. Experimental results on four open-domain instruction following benchmarks demonstrate the effectiveness of CodecLM over current state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools for processing natural language, but they can struggle to follow specific instructions. To solve this problem, researchers have started using LLMs to generate synthetic data that aligns with these instructions. However, most previous work has focused on generating diverse instructions and making the instructions more complex, without considering how this affects the quality of the generated data. This new framework, CodecLM, aims to change that by providing a way to adaptively generate high-quality synthetic data for LLM alignment with different instruction distributions and models. The approach uses LLMs as “codecs” to guide the data generation process, ensuring that the resulting data is tailored to the specific task at hand. |
Keywords
* Artificial intelligence * Alignment * Synthetic data
