Summary of Cocop: Enhancing Text Classification with Llm Through Code Completion Prompt, by Mohammad Mahdi Mohajeri et al.
CoCoP: Enhancing Text Classification with LLM through Code Completion Prompt
by Mohammad Mahdi Mohajeri, Mohammad Javad Dousti, Majid Nili Ahmadabadi
First submitted to arxiv on: 13 Nov 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 research proposes a novel method called CoCoP (Code Completion Prompt) that transforms text classification into a code completion task, leveraging large language models’ (LLMs’) capabilities in code-related tasks. The CoCoP method improves text classification performance across various datasets, including the SST2 dataset, by up to 20%. When combined with LLMs specifically designed for code-related tasks, such as CodeLLaMA, CoCoP demonstrates comparable or better performance than few-shot learning techniques while using significantly less model size. This approach has significant implications for natural language processing (NLP) and can be applied to a wide range of text classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how we can use large language models to help with something called “text classification.” Text classification is like putting things into categories, like emails or articles, based on what they say. The big idea here is that these models are really good at completing code snippets (small pieces of programming code). So, the researchers came up with a way to use this strength to improve text classification. They called it CoCoP (Code Completion Prompt). This method makes text classification better by using these large language models in a new way. It even works well when used with other special models designed just for coding tasks. |
Keywords
» Artificial intelligence » Few shot » Natural language processing » Nlp » Prompt » Text classification
