首个多语言承诺验证数据集，助力跨国企业环保承诺可信度评估。
ML-Promise: A Multilingual Dataset for Corporate Promise Verification
- 构建涵盖英法中日韩的多语言承诺验证数据集
- 提出基于检索增强生成的验证方法，提升跨语言可信度判断
- 聚焦企业ESG报告，对抗绿色洗白现象，适合政策与合规研究者
政治人物、企业领袖及公众人物作出的承诺对公众认知、信任及机构声誉具有重大影响。然而，承诺内容复杂且数量庞大，加之验证困难，亟需创新方法评估其可信度。本文提出承诺验证新范式，包括承诺识别、证据评估及验证时机判断等步骤。我们首次构建了多语言数据集ML-Promise，涵盖英语、法语、中文、日语和韩语，旨在深入验证承诺，尤其聚焦企业环境、社会与治理（ESG）报告中的承诺。随着企业环保贡献日益受关注，该数据集有助于应对绿色洗白等挑战。实验探索了文本与图像基线方法，检索增强生成（RAG）表现优异。本工作旨在推动跨语言、跨领域公共承诺问责机制的深入讨论。
原文摘要 · Abstract (English)
Promises made by politicians, corporate leaders, and public figures have a significant impact on public perception, trust, and institutional reputation. However, the complexity and volume of such commitments, coupled with difficulties in verifying their fulfillment, necessitate innovative methods for assessing their credibility. This paper introduces the concept of Promise Verification, a systematic approach involving steps such as promise identification, evidence assessment, and the evaluation of timing for verification. We propose the first multilingual dataset, ML-Promise, which includes English, French, Chinese, Japanese, and Korean, aimed at facilitating in-depth verification of promises, particularly in the context of Environmental, Social, and Governance (ESG) reports. Given the growing emphasis on corporate environmental contributions, this dataset addresses the challenge of evaluating corporate promises, especially in light of practices like greenwashing. Our findings also explore textual and image-based baselines, with promising results from retrieval-augmented generation (RAG) approaches. This work aims to foster further discourse on the accountability of public commitments across multiple languages and domains.
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