BenCSSmark: Making the Social Sciences Count in LLM Research

Problem
This position paper addresses the significant gap in the representation of social science tasks within existing large language model (LLM) benchmarks, which hampers both LLM evaluation and social scientific research. The authors argue that current benchmarks predominantly focus on technical tasks, neglecting the rich, context-sensitive datasets produced by social scientists. This under-representation limits the potential for LLMs to generalize across diverse domains, particularly as social sciences increasingly leverage AI technologies. The work is presented as a preprint and has not undergone peer review.

Method
The authors propose BenCSSmark, a novel benchmark designed to incorporate datasets annotated by computational social scientists. The benchmark aims to integrate social scientific methodologies and perspectives into the evaluation of LLMs. While specific architectural details, loss functions, and training compute are not disclosed, the emphasis is on the inclusion of rigorously annotated datasets that reflect the complexities of social science inquiries. The authors advocate for a collaborative approach, encouraging the integration of social science datasets into mainstream LLM evaluation frameworks to enhance model robustness and generalization.

Results
The paper does not present empirical results or quantitative comparisons against established baselines, as it primarily serves as a conceptual framework for integrating social science tasks into LLM benchmarks. The authors highlight the potential for improved model performance on both traditional and contemporary tasks across various disciplines, including history, sociology, political science, and economics, by leveraging social scientific datasets. However, specific performance metrics or effect sizes are not provided, as the focus is on advocating for the benchmark’s development rather than presenting experimental results.

Limitations
The authors acknowledge that the integration of social science datasets into LLM benchmarks may face challenges, such as the need for interdisciplinary collaboration and the potential for biases inherent in social scientific data. They do not address the scalability of BenCSSmark or the computational resources required for training LLMs on these new datasets. Additionally, the paper does not explore the implications of varying dataset quality or the representativeness of social science tasks in relation to broader AI applications.

Why it matters
The introduction of BenCSSmark has significant implications for the future of LLM research and application. By incorporating social scientific perspectives into benchmark design, the authors aim to foster the development of AI systems that are not only technically proficient but also socially relevant and ethically informed. This could lead to more robust AI models capable of addressing complex societal issues, ultimately enhancing the utility of LLMs in both academic and commercial contexts. The proposed benchmark may catalyze further interdisciplinary collaboration, bridging the gap between AI and social sciences, and encouraging the development of AI systems that are better aligned with human values and societal needs.

Authors: Arnault Chatelain, Étienne Ollion, Qianwen Guan, Diandra Fabre, Lorraine Goeuriot, Emile Chapuis, Abdelkrim Beloued, Marie Candito et al.
Source: arXiv:2605.04886
URL: https://arxiv.org/abs/2605.04886v1