Summary of Talec: Teach Your Llm to Evaluate in Specific Domain with In-house Criteria by Criteria Division and Zero-shot Plus Few-shot, By Kaiqi Zhang et al.
TALEC: Teach Your LLM to Evaluate in Specific Domain with In-house Criteria by Criteria Division and Zero-shot Plus Few-shot
by Kaiqi Zhang, Shuai Yuan, Honghan Zhao
First submitted to arxiv on: 25 Jun 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 model-based evaluation method, TALEC, to assess large language models (LLMs) in specific application domains, such as business-to-customer services. Currently, LLM evaluations rely heavily on manual assessment, which is costly and time-consuming. TALEC allows users to set their own evaluation criteria and leverages in-context learning (ICL) to teach a judge model these custom criteria. The approach combines zero-shot and few-shot learning to focus the judge model on more information. A prompt paradigm and engineering approach are also introduced to adjust and iterate the shots, enabling better understanding of complex criteria. Comparison with fine-tuning reveals that ICL can replace it. TALEC demonstrates a strong ability to accurately reflect human preferences, achieving over 80% correlation with human judgments, surpassing even inter-human correlation in some tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about creating a new way to test big language models (LLMs) that are used for business purposes. Right now, people have to manually check these models, which takes a lot of time and money. The new method, called TALEC, lets users set their own rules for testing the models and uses machine learning to help understand what those rules mean. This approach combines different techniques to make sure the model is focusing on the right information. By comparing this approach with another way of fine-tuning the model, researchers found that the new method works just as well or even better. The goal is to create a more efficient and accurate way to test these language models. |
Keywords
» Artificial intelligence » Few shot » Fine tuning » Machine learning » Prompt » Zero shot
