Domain-Specific Zero-Shot Question-Answering Task

A Domain-Specific Zero-Shot Question-Answering Task is a zero-shot question-answering task that is a domain-specific LLM-based question-answering task.

• Context:
  • measure: a Domain-Specific Zero-Shot Question-Answering Performance Measure.
  • It can be supported by a Domain-Specific Zero-Shot Question-Answering System (which can implement a domain-specific zero-shot question-answering algorithm).
  • It can be associated to a Domain-Specific Zero-Shot Question-Answering Evaluation Task.
  • It can be a KB-Using Domain-Specific Zero-Shot Question-Answering Task.
  • ...
• Example(s):
  • a Financial Document Question-Answering Task for financial document-related questions.
  • a Legal Question-Answering Task for law-related questions.
  • a Healthcare Question-Answering Task for healthcare-related questions.
  • Domain-Specific Task-Specific Zero-Shot Question-Answering, such as e-commerce customer support zero-shot question-answering.
  • ...
• Counter-Example(s):
  • Few-Shot Domain-Specific Question-Answering
  • General Zero-Shot Q&A, which does not focus on a specific domain but rather a wide range of general knowledge topics.
  • Domain-Specific Summarization, ...
• See: Zero-Shot Task, Zero-Shot Learning, Domain-Specific Knowledge.

---

---

Refrences

2023

• GBard
  • A Domain-Specific Zero-Shot Question-Answering Task (DSZQA) is an advanced artificial intelligence challenge. It involves an AI system answering specialized questions in a specific field like medicine, law, or art history, without any prior training specific to that domain. This task is akin to querying a knowledgeable library that instantly provides detailed, domain-specific answers. The task is characterized by being:
    • Domain-Specific: The AI focuses on a particular area, ensuring answers are specialized and relevant.
    • Zero-Shot: The AI receives no prior training on the specific questions, relying instead on its general language understanding and contextual learning abilities.
    • Question-Answering: The AI must understand and analyze the question in the context of the domain, and then deduce the most pertinent and accurate answer.

2023

• (Phogat et al., 2023) ⇒ Karmvir Singh Phogat, Chetan Harsha, Sridhar Dasaratha, Shashishekar Ramakrishna, and Sai Akhil Puranam. (2023). “Zero-Shot Question Answering over Financial Documents Using Large Language Models.” In: arXiv preprint arXiv:2311.14722. doi:10.48550/arXiv.2311.14722
  • NOTES:
    • It introduces a large language model (LLM) based approach to answer complex questions requiring multi-hop numerical reasoning over financial reports.
    • It proposes a new approach using zero-shot prompts for financial question answering with LLMs, eliminating the requirement for hand crafted examples.
    • It considers two baseline zero-shot prompting techniques: standard dual prompt (ZS-STD) and zero-shot chain-of-thought prompt (ZS-CoT).
    • It evaluates the approach on three financial question answering datasets using GPT models.

---