Chinese Dynamic Question Answering Benchmark: CDQA Introduction and Evaluation

Core Concepts

The author introduces CDQA, a Chinese Dynamic QA benchmark, to challenge Chinese LLMs with dynamic questions. Extensive experiments and analysis provide valuable insights for enhancing LLMs' capabilities.

Abstract

The content introduces CDQA, a Chinese Dynamic QA benchmark challenging LLMs with dynamic questions. It discusses the data construction pipeline, evaluation metrics, experiment results, prompt design impact, search engine comparison, and limitations. The study aims to improve LLM-driven applications for Chinese users.

Stats

"We obtain high-quality data through a pipeline that combines humans and models." "We have also evaluated mainstream and advanced Chinese LLMs on CDQA." "Results show that GPT-4 still ranks at the top with searched results from search engines." "GPT-4 answers with great care in vanilla prompts with lowest answer rates but high F1-recall scores." "Deepseek-67B-Chat has shown great performance as it surpasses GPT-4 on slow-changing and never-changing questions."

Quotes

"We believe that the benchmark we provide will become the key data resource for improving LLMs’ Chinese question-answering ability in the future." "Vanilla prompts outperform the other two kinds of prompts." "Google consistently outperforms Bing among all baseline models." "GPT-4 hallucinates more with more few-shot examples in CDQA." "Verbose explanation or expansion could increase hallucination especially when without evidence."

Key Insights Distilled From

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark

by Zhikun Xu,Yi... at arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19248.pdf

Deeper Inquiries

How can CDQA be adapted to evaluate LLMs in other languages?

To adapt CDQA for evaluating LLMs in other languages, a similar approach can be followed with some modifications. The key steps would involve: Data Collection: Gather latest news and information from sources specific to the target language. Entity Extraction and Query Generation: Utilize language-specific models for entity extraction and question generation. Manual Annotation: Employ native speakers or experts proficient in the target language for manual verification and annotation of question-answer pairs. Regular Updates: Ensure periodic updates of the dataset with current information relevant to the target language.

What are the implications of different prompt styles on reducing hallucinations in LLM responses?

Different prompt styles like Vanilla, Chain-of-Thought (CoT), and Rephrase-and-Respond (RaR) have varying effects on reducing hallucinations in LLM responses: Vanilla Prompt: Directly asking models to answer questions may lead to more cautious responses, potentially reducing hallucinations but at times limiting context understanding. CoT Prompt: Asking models to reason step by step before answering could enhance reasoning abilities, potentially leading to more accurate responses with reduced hallucination risks. RaR Prompt: Requesting models to rephrase questions might help them understand queries better, possibly resulting in clearer answers with fewer instances of hallucination.

How can the limitations of keeping CDQA updated be addressed effectively?

To address the challenge of keeping CDQA updated effectively, several strategies can be implemented: Implement an automated system that regularly scrapes up-to-date information from reliable sources related to Chinese Internet news. Utilize machine learning algorithms for real-time data processing and updating of question-answer pairs based on evolving knowledge trends. Establish a feedback loop mechanism where users can report outdated or incorrect answers, prompting manual review and updates by human annotators. Collaborate with domain experts who stay abreast of current events within Chinese contexts to ensure accuracy and relevance in dataset updates. These approaches combined will help maintain the relevancy and accuracy of CDQA over time despite dynamic changes in information landscapes.