APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching
<|reference_start|>APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching: Prompt engineering is an iterative procedure often requiring extensive manual effort to formulate suitable instructions for effectively directing large language models (LLMs) in specific tasks. Incorporating few-shot examples is a vital and effective approach to providing LLMs with precise instructions, leading to improved LLM performance. Nonetheless, identifying the most informative demonstrations for LLMs is labor-intensive, frequently entailing sifting through an extensive search space. In this demonstration, we showcase a human-in-the-loop tool called APE (Active Prompt Engineering) designed for refining prompts through active learning. Drawing inspiration from active learning, APE iteratively selects the most ambiguous examples for human feedback, which will be transformed into few-shot examples within the prompt. The demo recording can be found with the submission or be viewed at https://youtu.be/OwQ6MQx53-Y.<|reference_end|>
@article{qian2024ape:,
    title={APE: Active Learning-based Tooling for Finding Informative Few-shot
  Examples for LLM-based Entity Matching},
    author={Kun Qian and Yisi Sang and Farima Fatahi Bayat and Anton Belyi and
  Xianqi Chu and Yash Govind and Samira Khorshidi and Rahul Khot and Katherine
  Luna and Azadeh Nikfarjam and Xiaoguang Qi and Fei Wu and Xianhan Zhang and
  Yunyao Li},
    journal={arXiv preprint arXiv:2408.04637},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04637},
    primaryClass={cs.CL}
}