Summary of Commonit: Commonality-aware Instruction Tuning For Large Language Models Via Data Partitions, by Jun Rao et al.
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions
by Jun Rao, Xuebo Liu, Lian Lian, Shengjun Cheng, Yunjie Liao, Min Zhang
First submitted to arxiv on: 4 Oct 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 This study introduces a novel instruction tuning strategy, CommonIT, to enhance the ability of Large Language Models (LLMs) to follow commands. Unlike most works focusing on data mixing, this approach concentrates on enhancing model capabilities through data sampling during training. By clustering instruction datasets into distinct groups and ensuring each mini-batch consists solely of data from a single group, CommonIT brings about both data randomness across mini-batches and intra-batch data similarity. The study demonstrates the effectiveness of CommonIT in enhancing LLMs’ instruction-following capabilities on various IT datasets (FLAN, CoT, and Alpaca) and models (LLaMa2-7B, Qwen2-7B, LLaMa 13B, and BLOOM 7B). The results show an average improvement of 2.1% in the general domain, 5.2% in the special domain, and 3.8% on specific tasks. This approach has implications for improving the performance of LLMs in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers better understand what we want them to do. Right now, they can be tricky to work with because they don’t always follow instructions correctly. The scientists developed a new way to teach these computers how to follow instructions more accurately. They grouped the instructions into categories and then gave the computer a mix of similar tasks to practice on. This made the computer better at understanding what we want it to do. The results show that this method improved the computer’s performance by 2-5% in different areas. |
Keywords
» Artificial intelligence » Clustering » Instruction tuning » Llama
