Summary of Lbc: Language-based-classifier For Out-of-variable Generalization, by Kangjun Noh et al.
LBC: Language-Based-Classifier for Out-Of-Variable Generalization
by Kangjun Noh, Baekryun Seong, Hoyoon Byun, Youngjun Choi, Sungjin Song, Kyungwoo Song
First submitted to arxiv on: 20 Aug 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 paper proposes a novel approach to tabular data classification, leveraging Large Language Models (LLMs) to overcome limitations of traditional machine learning models (TMLs) like XGBoost. Specifically, the study focuses on Out-of-Variable (OOV) tasks, where LLMs excel in interpreting new variables without additional training. The proposed Language-Based-Classifier (LBC) combines three key strategies: categorical changes, advanced order and indicator features, and verbalizer-based mapping to classes during inference. By utilizing the pre-trained knowledge of LLMs, LBC outperforms TMLs on OOV tasks, as empirically and theoretically validated in this study. The LBC model is made publicly available at https://github.com/sksmssh/LBCforOOVGen. Key findings include the benefits of using LLMs for tabular data classification, particularly in handling OOV tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a special kind of AI called Large Language Models that can understand and work with text. This paper shows how these models can be used to improve a specific type of task called Out-of-Variable (OOV) tasks. OOV tasks are challenging because the model has never seen some of the variables before, but the Large Language Model is able to figure them out without needing more training. The authors propose a new way of using this special AI to classify tabular data, which is like organizing information into categories. This approach is better than traditional methods and can be used in many different areas. |
Keywords
» Artificial intelligence » Classification » Inference » Large language model » Machine learning » Xgboost
