Who's Keeping Score? Interactive Steering of LLM-Powered Scoring with Attune

[2608.14948] Who's Keeping Score? Interactive Steering of LLM-Powered Scoring with Attune Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Human-Computer Interaction arXiv:2608.14948 (cs) [Submitted on 15 Aug 2026] Title: Who's Keeping Score? Interactive Steering of LLM-Powered Scoring with Attune Authors: Bhavya Chopra , Meng Chen , Rebecca Dang , Chanbin Park , Shreya Shankar , Sepanta Zeighami , Bjoern Hartmann , Aditya Parameswaran View a PDF of the paper titled Who's Keeping Score? Interactive Steering of LLM-Powered Scoring with Attune, by Bhavya Chopra and 7 other authors View PDF HTML (experimental) Abstract: Large language models (LLMs) are increasingly used to score text records at scale (e.g., rating candidate resumes on a 1-5 scale). However, existing LLM-powered approaches do not account for the fact that effective scoring requires both holistic understanding of records and locally consistent judgments across similar ones. We present Attune, a mixed-initiative system for steerable LLM-powered scoring. Given a task description and scoring range, Attune performs pairwise comparisons across records to develop a global understanding first, and then resolves these comparisons into consistent score assignments-deriving scoring criteria and rules bottom-up in the process. These serve as shared representations of scoring logic that users can inspect and edit. Based on insights from a formative study (n = 12), Attune's interface introduces novel steering interactions that allow users to deterministically refine scoring logic. Users can provide examples, directly edit criteria, rules, or target distributions, and give natural language feedback-with all refinements compiling into constraints that guide re-scoring. We validate our approach through a technical evaluation across three workloads and a user study with domain experts (n = 8) in healthcare, law, education, and AI evaluation. Comments: 18 pages, To appear at ACM UIST 2026 Subjects: Human-Computer Interaction (cs.HC) ACM classes: H.5.2 Cite as: arXiv:2608.14948 [cs.HC] (or arXiv:2608.14948v1 [cs.HC] for this version) https://doi.org/10.48550/arXiv.2608.14948 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1145/3830398.3830567 Focus to learn more DOI(s) linking to related resources Submission history From: Bhavya Chopra [ view email ] [v1] Sat, 15 Aug 2026 00:06:02 UTC (4,223 KB) Full-text links: Access Paper: View a PDF of the paper titled Who's Keeping Score? Interactive Steering of LLM-Powered Scoring with Attune, by Bhavya Chopra and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.HC < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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