Domain Weights behind a Factuality Benchmark Average
Abstract
An aggregate artificial-intelligence benchmark score depends on the mix of tasks that produced it. Using released FELM response annotations, this article compares domain-specific error shares and estimates a Logit model with domain, response length, and segment-count controls. The world-knowledge domain has a higher adjusted error association than mathematics in the selected specification. Equal weighting of the five observed domains changes the overall error share only slightly, but the small aggregate change hides substantial domain differences. The paper connects that finding with contextual selection, heterogeneous transfer, robust optimization, and parameter-efficient adaptation. It treats transportability as an evidence question rather than an automatic consequence of a favorable benchmark mean.
References
- Y. Wang, K. Zhang, M. Wang, S. Gao, and N. Yin, “USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation,” Proceedings of the 2026 ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2026.
- S. Chen, Y. Zhao, J. Zhang, I.-C. Chern, S. Gao, P. Liu, and J. He, “FELM: Benchmarking Factuality Evaluation of Large Language Models,” Advances in Neural Information Processing Systems, pp. 44502–44523, 2023.
- Tong, H., Xu, G., & Chen, Y. (2026, May). Hadamard Tensor Ring for Efficient Low-Rank Fine-Tuning. In ICASSP 2026-2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Zhou, Yongxi, Wenbo Ye, Yuanzhe Liu, Zihan Dong, and Junwei Yao. “Style Over Substance: Content-Invariant Wrappers Flip LLM Safety-Judge Verdicts.” arXiv, 2026, arXiv:2609.08236. https://arxiv.org/abs/2609.08236.
- White, Halbert. “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity.” Econometrica, vol. 48, no. 4, 1980, pp. 817–838. https://doi.org/10.2307/1912934.
- Cameron, A. Colin, and Douglas L. Miller. “A Practitioner’s Guide to Cluster-Robust Inference.” Journal of Human Resources, vol. 50, no. 2, 2015, pp. 317–372. https://doi.org/10.3368/jhr.50.2.317.
- Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, PMLR, vol. 70, 2017, pp. 1321–1330. https://proceedings.mlr.press/v70/guo17a.html.
- Geifman, Yonatan, and Ran El-Yaniv. “Selective Classification for Deep Neural Networks.” Advances in Neural Information Processing Systems, vol. 30, 2017. https://arxiv.org/abs/1705.08500.
- Ovadia, Yaniv, et al. “Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift.” Advances in Neural Information Processing Systems, vol. 32, 2019. https://arxiv.org/abs/1906.02530.
- Lewis, Patrick, et al. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems, vol. 33, 2020, pp. 9459–9474. https://arxiv.org/abs/2005.11401.
- Hu, Edward J., et al. “LoRA: Low-Rank Adaptation of Large Language Models.” International Conference on Learning Representations, 2022. https://openreview.net/forum?id=nZeVKeeFYf9.
- Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd ed., MIT Press, 2018. http://incompleteideas.net/book/the-book-2nd.html.