SemBench: A Universal Semantic Framework for LLM Evaluation
arXiv:2603.11687v1 Announce Type: new Abstract: Recent progress in Natural Language Processing (NLP) has been driven by the emergence of Large Language Models (LLMs), which exhibit remarkable generative and reasoning capabilities. However, despite their success, evaluating the true semantic understanding of these models remains a persistent challenge. Traditional benchmarks such as Word-in-Context (WiC) effectively probe this capability, but their creation is resource-intensive and often limited to high-resource languages. In this paper, we introduce SemBench, a framework for automatically generating synthetic benchmarks that assess the semantic competence of LLMs using only dictionary sense definitions and a sentence encoder. This approach eliminates the need for curated example sentences, making it both scalable and language-independent. We evaluate SemBench in three languages (English, Spanish, and Basque) spanning different levels of linguistic resources, and across a wide range o
arXiv:2603.11687v1 Announce Type: new Abstract: Recent progress in Natural Language Processing (NLP) has been driven by the emergence of Large Language Models (LLMs), which exhibit remarkable generative and reasoning capabilities. However, despite their success, evaluating the true semantic understanding of these models remains a persistent challenge. Traditional benchmarks such as Word-in-Context (WiC) effectively probe this capability, but their creation is resource-intensive and often limited to high-resource languages. In this paper, we introduce SemBench, a framework for automatically generating synthetic benchmarks that assess the semantic competence of LLMs using only dictionary sense definitions and a sentence encoder. This approach eliminates the need for curated example sentences, making it both scalable and language-independent. We evaluate SemBench in three languages (English, Spanish, and Basque) spanning different levels of linguistic resources, and across a wide range of LLMs. Our results show that rankings derived from SemBench strongly correlate with those obtained from standard WiC datasets. Furthermore, our analysis demonstrates that only a small number of examples is required to achieve stable and meaningful rankings. Overall, SemBench provides a lightweight, adaptable, and data-efficient framework for cross-lingual evaluation of semantic understanding in LLMs.
Executive Summary
The authors introduce SemBench, a novel framework for evaluating the semantic understanding of Large Language Models (LLMs) in a universal and automatic manner. This approach leverages dictionary sense definitions and a sentence encoder to generate synthetic benchmarks, eliminating the need for curated example sentences and making it scalable and language-independent. The authors demonstrate the effectiveness of SemBench through evaluations in three languages, showing strong correlations with standard benchmarks and robust rankings with a small number of examples. SemBench offers a lightweight and adaptable framework for cross-lingual LLM evaluation, with significant implications for NLP research and applications.
Key Points
- ▸ SemBench is a universal framework for evaluating LLM semantic understanding.
- ▸ It generates synthetic benchmarks using dictionary sense definitions and a sentence encoder.
- ▸ SemBench achieves strong correlations with standard benchmarks and robust rankings with a small number of examples.
Merits
Scalability and Language-Independence
SemBench's ability to generate benchmarks without curated example sentences and in multiple languages makes it a valuable tool for NLP research and applications.
Efficiency and Effectiveness
SemBench achieves strong correlations with standard benchmarks and robust rankings with a small number of examples, demonstrating its efficiency and effectiveness.
Adaptability and Cross-Lingual Evaluation
SemBench's adaptable framework enables cross-lingual evaluation of LLMs, facilitating the development of more comprehensive and inclusive NLP systems.
Demerits
Overreliance on Dictionary Sense Definitions
SemBench's reliance on dictionary sense definitions might not capture the full complexity of semantic understanding, potentially leading to limitations in its evaluation capabilities.
Limited Generalizability to Other NLP Tasks
SemBench's focus on semantic understanding might not generalize well to other NLP tasks, such as sentiment analysis or named entity recognition.
Potential for Biased Benchmarks
The use of dictionary sense definitions and a sentence encoder might introduce biases in the generated benchmarks, potentially affecting the accuracy of LLM evaluations.
Expert Commentary
The introduction of SemBench represents a significant advancement in the field of NLP evaluation. By providing a universal and automatic framework for assessing LLM semantic understanding, SemBench addresses a long-standing challenge in NLP research. Its ability to generate synthetic benchmarks and achieve strong correlations with standard benchmarks demonstrates its efficiency and effectiveness. However, it is essential to acknowledge the potential limitations of SemBench, such as its reliance on dictionary sense definitions and limited generalizability to other NLP tasks. Nevertheless, SemBench's adaptability and cross-lingual evaluation capabilities make it a valuable tool for NLP research and applications. As the field of NLP continues to evolve, SemBench is likely to play a crucial role in shaping the development of more comprehensive and inclusive NLP systems.
Recommendations
- ✓ Further research is needed to explore the limitations and potential biases of SemBench.
- ✓ The development of additional benchmarks and evaluation methods that complement SemBench's approach is essential for a more comprehensive understanding of LLM semantic understanding.