Friday 31 January 2025
The quest for trustworthy AI has been a long-standing challenge in the field of artificial intelligence. To achieve this, researchers have been developing various benchmarks and datasets that can assess the performance and capabilities of large language models (LLMs). A recent paper presents an extensive overview of these benchmarks and datasets, providing a comprehensive framework for evaluating the trustworthiness of LLMs.
The authors highlight the importance of assessing the technical robustness and safety of AI systems. They identify seven key requirements that must be met to ensure trustworthy AI: human agency and oversight, technical robustness and safety, privacy and data governance, transparency, diversity, non-discrimination, and fairness, societal and environmental well-being, and accountability.
To address these requirements, the authors present a range of benchmarks and datasets that can evaluate the performance of LLMs in various domains. These include natural language understanding (NLU) tasks such as reading comprehension, question answering, and text classification; mathematical problem-solving; and logical reasoning.
The paper highlights several key challenges in evaluating the trustworthiness of LLMs. One major challenge is ensuring that the benchmarks are diverse and representative of real-world scenarios. Another challenge is preventing overfitting and ensuring that the models can generalize well to new data.
To address these challenges, the authors propose a framework for developing and evaluating trustworthy AI systems. This framework includes several key components, such as human oversight and feedback mechanisms, technical robustness and safety measures, and transparency and explainability features.
The paper also presents several case studies that demonstrate the effectiveness of this framework in real-world scenarios. For example, one study shows how a LLM can be used to provide personalized recommendations for patients with chronic diseases.
Overall, the authors’ work provides a comprehensive overview of the current state of the art in developing trustworthy AI systems. The paper highlights the importance of addressing technical robustness and safety concerns, as well as ensuring transparency and explainability in AI decision-making processes.
Cite this article: “Evaluating Trustworthiness in Large Language Models”, The Science Archive, 2025.
Artificial Intelligence, Trustworthy Ai, Language Models, Benchmarks, Datasets, Technical Robustness, Safety, Transparency, Explainability, Accountability
Reference: Todor Ivanov, Valeri Penchev, “AI Benchmarks and Datasets for LLM Evaluation” (2024).
