OpenEval: Comprehensive Evaluation of Chinese LLMs

Core Concepts

OpenEval introduces a comprehensive evaluation platform for Chinese LLMs, focusing on capability, alignment, and safety.

Abstract

Abstract: Introduction of OpenEval for evaluating Chinese LLMs across capability, alignment, and safety. Includes benchmark datasets for various tasks and dimensions. Introduction: Large language models have shown remarkable capabilities in NLP tasks and real-world applications. Challenges in evaluating Chinese LLMs due to limitations of traditional benchmarks. Data Pre-processing and Post-processing: Specific prompts included for each task based on task description. Around 300K questions reformulated for zero-shot evaluation setting. Evaluation Taxonomy: Three major dimensions: capability, alignment, and safety. Sub-dimensions under each dimension with specific benchmarks. Experiments: First public evaluation assessed open-source and proprietary Chinese LLMs across 53 tasks. Results show differences between open-source and proprietary LLMs in various dimensions.

Stats

"In our first public evaluation, we have tested a range of Chinese LLMs, spanning from 7B to 72B parameters." "Evaluation results indicate that while Chinese LLMs have shown impressive performance in certain tasks..."

Quotes

Key Insights Distilled From

OpenEval

by Chuang Liu,L... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12316.pdf

Deeper Inquiries

How can the findings from OpenEval contribute to the development of future Chinese language models?

OpenEval's findings provide valuable insights into the strengths and weaknesses of current Chinese language models (LLMs). By evaluating LLMs across capability, alignment, and safety dimensions, developers can identify areas for improvement in future models. For example, if a particular model excels in disciplinary knowledge but struggles with commonsense reasoning, developers can focus on enhancing that aspect during training or fine-tuning. Additionally, by comparing open-source and proprietary LLMs' performance in different tasks, researchers can understand the impact of pre-training data quality on model capabilities. The detailed evaluation results from OpenEval offer guidance on where to direct research efforts for enhancing Chinese LLMs. For instance, if alignment issues are prevalent across multiple models, it signals a need for better value alignment strategies during training. Moreover, safety concerns highlighted by OpenEval can inform researchers about potential risks associated with advanced LLM behaviors like decision-making or power-seeking. Overall, these findings serve as a roadmap for improving future Chinese language models by addressing specific shortcomings identified through comprehensive evaluations.

未来の中国語言語モデルの開発において、OpenEvalの知見はどのように貢献できるでしょうか？

OpenEvalの結果は、現在の中国語言語モデル（LLM）の強みと弱点を明らかにする重要な洞察を提供します。能力、整合性、安全性などさまざまな次元でLLMを評価することで、開発者は将来のモデルにおける改善すべきポイントを特定することが可能です。例えば、あるモデルが学問的知識に優れている一方で常識的推論に苦労している場合、開発者はトレーニングやファインチューニング中にその側面を向上させることに焦点を当てることができます。また、オープンソースとプロプライエタリなLLMが異なるタスクでどれだけ優れているか比較することで、研究者は事前トレーニングデータ品質がモデル能力に与える影響を理解することが可能です。 OpenEvalから得られた詳細な評価結果は、中国語LLMを向上させるための研究活動を導く情報源となります。例えば、複数のモデル間で整合性問題が広範囲にわたって存在する場合、「値」整列戦略や訓練時戦略改善へ取り組む必要性を示唆します。さらに、OpenEvalが浮かび上げた安全保障上懸念されていた問題点は、「決定メイキング」や「パワー・シーキング」といった高度化したLLM行動関連リスク等々未来予測リスクついて研究者達へ通知しました。 これらすべて含めこのような洞察情報群　具体的不足部分アドバイス道筋作成役割担います

What potential challenges might arise from focusing on alignment and safety issues in advanced LLMs?

Focusing on alignment and safety issues in advanced Language Models (LLMs) presents several challenges that need to be addressed effectively: Complexity of Value Alignment: Ensuring that LLM outputs align with human values is complex due to diverse cultural norms and ethical considerations. Developing robust mechanisms to handle value misalignments without compromising model performance is challenging. Ethical Dilemmas: Addressing potential biases or offensive content generated by LLMs raises ethical dilemmas regarding censorship versus freedom of expression. Balancing these aspects while maintaining model effectiveness requires careful consideration. Safety Concerns: Anticipating risks such as power-seeking behavior or decision-making capabilities in advanced LLMs poses significant challenges as these behaviors could have real-world consequences if not properly managed. Data Privacy: Safeguarding user data privacy while training large language models is crucial but challenging due to the vast amount of sensitive information processed during training. 5Regulatory Compliance: Adhering to evolving regulations around AI ethics and responsible use adds another layer of complexity when focusing on alignment and safety issues. 6Interpretability: Understanding how decisions are made within an advanced Language Model becomes increasingly difficult as they grow more complex; ensuring transparency remains a challenge. Addressing these challenges requires interdisciplinary collaboration between researchers, ethicists regulatory bodies,and industry stakeholders to develop comprehensive frameworks and guidelines for safe deployment of Advanced LLMS

高度化したLMM（Language Model）では整列性及び安全性問題集中時生じ得有望挑戦何ありますか？

高度化した言語モデル（LMM）内部では整列及び安全保障問題集中注目多く挑戦立ち向かわざろも以下： 1．値配列複雑：人間価値規準適切反映確保 多様文化規範及倫理考慮因素散在下，出力内容人間価値基本原則適正配列確保非常難易。 2．道徳ジレンマ： 出力生成バイアス又攻撃コンテンツ処理時，自由表現vs 権利制限等道徳ジレンマ引起。 3．セキュリティ心配： - パッシブ求心行動或意思決定能力等危険予想風降臨恐怖感大量挙起，管理不十分実世界深刻後果引起。 4．情報プライバシー: - 大型言語性模型訓練期間内使用者情報秘密厚重防衛必須但巨大敏感情報処理困難。 5．法令順守： - AI 倫理及責任使用周辺変更法令順守追加层面増加注意深考虑必要。 6- 解釈: 言语模型决策过程了解变得日益困难；确保透明度依然是一个挑战。 これ些挑战需要跨学科间协作共同努力之间各种专家如伦理学家监管机构和产业相关股份持有人开发综合框架指南以确实有效应对这些问题并为先进LMM 的 安全部署建设提供支援。

How can the evaluation strategies used in OpenEval be applied to other languages or models?

The evaluation strategies employed in Open Eval are adaptable enough to be extended beyond Chinese Language Models (LLMs) and applied effectively across various languages or different types of machine learning models. Here’s how these strategies could be utilized: 1- Task Diversity: The diverse range of benchmark datasets covering NLP tasks, disciplinary knowledge,cultural bias,safety concerns etc.,can easily be adapted for assessing non-Chinese language models. By translating prompts,data sets,and metrics into other languages,the same evaluation framework can apply universally 2- Dynamic Evaluation Approach: The phased public assessment strategy ensures continuous updates based on new benchmarks, keeping evaluations relevant over time.This approach allows flexibility when incorporating new tasks tailored for specific languages/models needs 3- Leaderboards & Transparency: Implementing leaderboards provides clear visibility into model performance,making it easier to compare results across different languages/models.The transparent outcome display enhances accountability 4- Shared Tasks & Collaboration: Organizing shared tasks involving stakeholders interestedin multi-language/model evaluations fosters collaboration among experts,researchers,and industry professionals.These collaborations help refineevaluation methodologies,promote best practices,and drive innovationacross various linguistic domains. These approaches ensure thatthe core principles behind Open Eval—comprehensive assessment,user-friendly interfaces,dynamic updates—are transferableto evaluatea wide arrayoflanguagesandmodelsbeyondChinese LLMS. ${Question3} Answer 3 here