Automating Dataset Updates for Reliable and Timely Evaluation

핵심 개념

The author proposes two strategies, mimicking and extending, to automate dataset updates for reliable and timely evaluation by addressing data leakage issues and controlling sample difficulty.

초록

The paper introduces two strategies, mimicking and extending, to automate dataset updates for reliable evaluation. Mimicking generates new samples resembling existing ones, while extending adjusts the difficulty of generated samples. Extensive experiments demonstrate the effectiveness and stability of these strategies in dealing with data leakage issues. The study also explores how cognitive levels and entity popularity can control question difficulty in the extended datasets.

통계

Large Language Models (LLMs) are facing serious evaluation challenges due to data leakage issues causing over-estimation on existing benchmarks.
The proposed mimicking strategy employs LLMs to create new samples resembling existing ones to mitigate data leakage issues.
The extending strategy adjusts the difficulty of generated samples according to varying cognitive levels.
Experiments show high stability in both mimicking and extending strategies across multiple iterations.
Human evaluation results confirm the reliability of both strategies in generating high-quality samples.
Models exhibit significant performance variations across different cognitive levels, with GPT-4 showing strong performance overall.

인용구

"Due to expanding capabilities and pre-training data, Large Language Models (LLMs) face increasingly serious evaluation challenges."
"Our experiments demonstrate the stability of our evaluation strategies across multiple instances."
"The popularity of seed input can be manipulated to control the difficulty of generated questions."

핵심 통찰 요약

Have Seen Me Before? Automating Dataset Updates Towards Reliable and Timely Evaluation

by Jiahao Ying,... 게시일 arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.11894.pdf

더 깊은 질문

Can introducing external knowledge improve dataset updates beyond what is explored in this study?

Introducing external knowledge can indeed enhance dataset updates beyond the strategies explored in this study. By incorporating relevant information from external sources, such as domain-specific data or expert knowledge, the quality and diversity of the generated samples can be significantly improved. This additional context can help create more challenging and realistic evaluation scenarios for models, leading to better performance assessment and training.

How might closed-source models benefit from automated dataset updates compared to open-source models?

Closed-source models could benefit greatly from automated dataset updates compared to open-source models due to their restricted access to training data. Automated updates provide a cost-effective way for closed-source models to continuously refresh their datasets without relying on manual curation or extensive resources. This ensures that these models stay up-to-date with evolving trends and challenges in Natural Language Processing, ultimately improving their performance and adaptability.

What implications do these automated dataset update strategies have for future advancements in Natural Language Processing?

The automated dataset update strategies presented in this study have significant implications for future advancements in Natural Language Processing (NLP). These strategies offer a systematic approach to addressing data leakage issues, enhancing evaluation reliability, and controlling sample difficulty levels. By automating the process of updating datasets, researchers and developers can focus more on refining model capabilities rather than spending time on manual data curation. This efficiency accelerates progress in NLP research by enabling faster experimentation cycles, promoting innovation, and advancing the development of more robust language models with improved generalization abilities.