Position and Length Associations after Removing Response Means
Abstract
The question concerns within-response segment position association. The secondary ordinary least squares analysis contains 4,294 observations and uses cluster covariance. The coefficient on position is 0.0612, with a 95 percent interval from 0.0313 to 0.0910. Removing response means compares segments inside the same retained response, controlling its time-invariant context. It does not control segment-specific difficulty or make position random. Dual dynamics robustness motivates separate shift mechanisms, while this static annotation analysis contains neither training transitions nor deployment perturbations. The within-response coefficient therefore remains a descriptive factuality relationship. The estimates describe the released collection and its conditioning set. No new data collection, simulator experiment, or deployment-policy evaluation is claimed.
References
Z. Qiao, R. Yang, J. Lyu, X. Li, Z. Dai, Z. Yang, S. Gao, and S. Qiu, “Dual-Robust Cross-Domain Offline Reinforcement Learning Against Dynamics Shifts,” Proceedings of the 2026 International Conference on Learning Representations, 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)
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.
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- 2026-10-15 (2)
- 2026-09-28 (1)