# Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization

arXiv: https://arxiv.org/abs/2610.14757 · HTML: https://mlsanity.com/paper/2610.14757/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 75 references that are in the map.
- First posted on arXiv 10 Oct 2026.

## Builds on (most cited first, 22 of 22)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Neural Text Generation with Unlikelihood Training](https://mlsanity.com/paper/1908.04319/) (2019)
- [Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization](https://mlsanity.com/paper/2006.05078/) (2020)
- [Re-evaluating Evaluation in Text Summarization](https://mlsanity.com/paper/2010.07100/) (2020)
- [Constrained Bayesian Optimization with Noisy Experiments](https://mlsanity.com/paper/1706.07094/) (2017)
- [Preferential Bayesian Optimization](https://mlsanity.com/paper/1704.03651/) (2017)
- [The reparameterization trick for acquisition functions](https://mlsanity.com/paper/1712.00424/) (2017)
- [How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models](https://mlsanity.com/paper/2209.01390/) (2022)
- [Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes](https://mlsanity.com/paper/2203.11382/) (2022)
- [Sequential Gallery for Interactive Visual Design Optimization](https://mlsanity.com/paper/2005.04107/) (2020)
- [Projective Preferential Bayesian Optimization](https://mlsanity.com/paper/2002.03113/) (2020)
- [Top-k Ranking Bayesian Optimization](https://mlsanity.com/paper/2012.10688/) (2020)
- [Preferential Batch Bayesian Optimization](https://mlsanity.com/paper/2003.11435/) (2020)
- [Cost-Effective HITs for Relative Similarity Comparisons](https://mlsanity.com/paper/1404.3291/) (2014)
- [Learning Context-Dependent Choice Functions](https://mlsanity.com/paper/1901.10860/) (2019)
- [Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques](https://mlsanity.com/paper/2204.07641/) (2022)
- [Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation](https://mlsanity.com/paper/1911.10183/) (2019)
- [The Human in the Infinite Loop: A Case Study on Revealing and Explaining Human-AI Interaction Loop Failures](https://mlsanity.com/paper/2207.12761/) (2022)
- [Choice functions based multi-objective Bayesian optimisation](https://mlsanity.com/paper/2110.08217/) (2021)
- [The Impact of Expertise in the Loop for Exploring Machine Rationality](https://mlsanity.com/paper/2302.05665/) (2023)

## Abstract (arXiv)

Human-in-the-loop optimization refers to systems that present choices to a human decision-maker and query their judgment to optimize system parameters iteratively. The user interface typically provides either an absolute rating scale or asks for a preferred option. However, these approaches can potentially suffer from calibration problems, inconsistencies, and completeness violations of user preferences in such a sequential evaluation. This paper revisits the design space of opinion measurement interfaces for human-in-the-loop optimization. We conducted a study (N=360) using text summarization and image color enhancement tasks to evaluate human sequential feedback in six representative opinion measurement interfaces. Based on our analysis, we recommend using a listwise approach to support sequential opinion measurement, which counterweighs individual rating and preferential choice limitations and conveys more information for subsequent use. We further discuss the trade-offs in different user interface designs and provide guidelines to inform future research.