APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching

• URL: http://arxiv.org/abs/2408.04637v1
• Date: Mon, 29 Jul 2024 22:22:50 GMT
• Title: APE: Active Learning-based Tooling for Finding Informative Few-shot Examples for LLM-based Entity Matching
• Authors: Kun Qian, Yisi Sang, Farima Fatahi Bayat, Anton Belyi, Xianqi Chu, Yash Govind, Samira Khorshidi, Rahul Khot, Katherine Luna, Azadeh Nikfarjam, Xiaoguang Qi, Fei Wu, Xianhan Zhang, Yunyao Li,
• Abstract summary: In this demonstration, we showcase a human-in-the-loop tool called APE (Active Prompt Engineering) APE iteratively selects the most ambiguous examples for human feedback, which will be transformed into few-shot examples within the prompt.
• Score: 14.113933201562157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Large Language Models Know What Makes Exemplary Contexts [42.90814615222177]
  In-context learning (ICL) has proven to be a significant capability with the advancement of Large Language models (LLMs) This paper presents a unified framework for LLMs that allows them to self-select influential in-context examples to compose their contexts.
  arXiv  Detail & Related papers  (2024-08-14T12:32:41Z)
• MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization [73.7779735046424]
  We show that different prompts should be adapted to different Large Language Models (LLM) to enhance their capabilities across various downstream tasks in NLP. We then propose a model-adaptive prompt (MAPO) method that optimize the original prompts for each specific LLM in downstream tasks.
  arXiv  Detail & Related papers  (2024-07-04T18:39:59Z)
• Show, Don't Tell: Aligning Language Models with Demonstrated Feedback [54.10302745921713]
  Demonstration ITerated Task Optimization (DITTO) directly aligns language model outputs to a user's demonstrated behaviors. We evaluate DITTO's ability to learn fine-grained style and task alignment across domains such as news articles, emails, and blog posts.
  arXiv  Detail & Related papers  (2024-06-02T23:13:56Z)
• Efficient Prompting Methods for Large Language Models: A Survey [50.171011917404485]
  Prompting has become a mainstream paradigm for adapting large language models (LLMs) to specific natural language processing tasks. This approach brings the additional computational burden of model inference and human effort to guide and control the behavior of LLMs. We present the basic concepts of prompting, review the advances for efficient prompting, and highlight future research directions.
  arXiv  Detail & Related papers  (2024-04-01T12:19:08Z)
• Helping Language Models Learn More: Multi-dimensional Task Prompt for Few-shot Tuning [36.14688633670085]
  We propose MTPrompt, a multi-dimensional task prompt learning method based on task-related object, summary, and task description information. By automatically building and searching for appropriate prompts, our proposed MTPrompt achieves the best results on few-shot samples setting and five different datasets.
  arXiv  Detail & Related papers  (2023-12-13T10:00:44Z)
• Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering [9.158919909909146]
  In this paper, we propose a novel way of employing labeled data such as it informs LLM of some undesired output. Experiments on three multi-span question answering datasets and a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM's in-context learning performance.
  arXiv  Detail & Related papers  (2023-06-07T15:20:24Z)
• OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning [49.38867353135258]
  We propose OverPrompt, leveraging the in-context learning capability of LLMs to handle multiple task inputs. Our experiments show that OverPrompt can achieve cost-efficient zero-shot classification without causing significant detriment to task performance.
  arXiv  Detail & Related papers  (2023-05-24T10:08:04Z)
• Multi-Task Instruction Tuning of LLaMa for Specific Scenarios: A Preliminary Study on Writing Assistance [60.40541387785977]
  Small foundational models can display remarkable proficiency in tackling diverse tasks when fine-tuned using instruction-driven data. In this work, we investigate a practical problem setting where the primary focus is on one or a few particular tasks rather than general-purpose instruction following. Experimental results show that fine-tuning LLaMA on writing instruction data significantly improves its ability on writing tasks.
  arXiv  Detail & Related papers  (2023-05-22T16:56:44Z)
• ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction [56.790794611002106]
  Large language models (LLMs) have demonstrated remarkable results in various natural language processing (NLP) tasks with in-context learning. We propose a simple but effective in-context learning framework called ICL-D3IE. Specifically, we extract the most difficult and distinct segments from hard training documents as hard demonstrations.
  arXiv  Detail & Related papers  (2023-03-09T06:24:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.