Human Feedback Types
strongRubric Rating
Directly usable for protocol triage.
"Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories."
HFEPX · Eval paper review
Augmenting Rating-Scale Measures with Text-Derived Items Using the Information-Determined Scoring (IDS) Framework
Joe Watson, Ivan O'Connor, Chia-Wen Chen, Luning Sun +2 more
Published
Oct 9, 2025
Citations
0
Trust level
Moderate
Usefulness score
77/100 (High)
Extraction confidence
70% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Mar 19, 2026
Should you rely on this paper?
This paper has strong direct human-feedback and evaluation protocol signal and is suitable as a primary eval pipeline reference.
Use this for comparison and orientation, not as your only source.
Best use
Primary protocol reference for eval design
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Validate the evaluation procedure and quality controls in the full paper before operational use.
Main weakness
No major weakness surfaced.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
77/100
High-confidence candidate
Use this as a primary source when designing or comparing eval protocols.
If you are doing eval pipeline work, start here
Abstract
Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories. Although rich, unstructured text is often captured alongside these scales, it rarely contributes to measuring the target trait because it lacks direct mapping to the latent scale. We introduce the Information-Determined Scoring (IDS) framework, where large language models (LLMs) score free-text responses with simple prompts to generate candidate items that are co-calibrated with a baseline scale and retained based on the psychometric information they provide about the target trait. This marks a conceptual departure from traditional automated text scoring by prioritising information gain over fidelity to expert rubrics or human-annotated data. Using depression as a case study, we developed and tested the method in upper-secondary students (n = 693) and a matched synthetic dataset (n = 3,000). Across held-out test sets, augmenting a 19-item rating-scale measure with LLM-derived items yielded significant improvements in measurement precision and accuracy, and stronger convergent validity with an external suicidality measure throughout the adaptive test. In adaptive simulations, LLM-derived items contributed information equivalent to adding up to 6.3 and 16.0 rating-scale items in real and synthetic data, respectively. This enabled earlier high-precision measurement: after 10 items, 46.3% of respondents reached SE <= .3 under the strongest augmented test compared with 35.5% at baseline in real data, and 60.4% versus 34.7% in synthetic data. These findings illustrate how the IDS framework leverages unstructured text to enhance existing psychological measures, with applications in clinical health and beyond.
What we could verify
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Evaluation Modes
strongAutomatic Metrics, Simulation Env
Includes extracted eval setup.
"Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories."
Quality Controls
missingNot reported
No explicit QC controls found.
"Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories."
Reported Metrics
strongAccuracy, Precision
Useful for evaluation criteria comparison.
"Across held-out test sets, augmenting a 19-item rating-scale measure with LLM-derived items yielded significant improvements in measurement precision and accuracy, and stronger convergent validity with an external suicidality measure throughout the adaptive test."
Rater Population
strongDomain Experts
Helpful for staffing comparability.
"This marks a conceptual departure from traditional automated text scoring by prioritising information gain over fidelity to expert rubrics or human-annotated data."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
accuracyprecision
Human feedback details
- Uses human feedback
- Yes
- Feedback types
- Rubric Rating
- Rater population
- Domain Experts
- Unit of annotation
- Multi Dim Rubric
- Expertise required
- Medicine
Evaluation details
- Evaluation modes
- Automatic Metrics, Simulation Env
- Agentic eval
- None
- Quality controls
- Not reported
- Evidence quality
- Moderate
- Use this page as
- Primary protocol reference for eval design
Research brief
Metadata summary
Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Psychological assessments commonly rely on rating-scale items, which require respondents to condense complex experiences into predefined categories.
- Although rich, unstructured text is often captured alongside these scales, it rarely contributes to measuring the target trait because it lacks direct mapping to the latent scale.
- We introduce the Information-Determined Scoring (IDS) framework, where large language models (LLMs) score free-text responses with simple prompts to generate candidate items that are co-calibrated with a baseline scale and retained based on the psychometric information they provide about the target trait.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Validate inferred eval signals (Automatic metrics) against the full paper.
- Use related-paper links to find stronger protocol-specific references.
Caveats
- Generated from abstract + metadata only; no PDF parsing.
- Signals below are heuristic and may miss details reported outside the abstract.
Recommended queries
Contribution summary
- We introduce the Information-Determined Scoring (IDS) framework, where large language models (LLMs) score free-text responses with simple prompts to generate candidate items that are co-calibrated with a baseline scale and retained based on…
- This marks a conceptual departure from traditional automated text scoring by prioritising information gain over fidelity to expert rubrics or human-annotated data.
- Across held-out test sets, augmenting a 19-item rating-scale measure with LLM-derived items yielded significant improvements in measurement precision and accuracy, and stronger convergent validity with an external suicidality measure…
Why it matters for eval
- This marks a conceptual departure from traditional automated text scoring by prioritising information gain over fidelity to expert rubrics or human-annotated data.
Researcher checklist
-
Human feedback protocol is explicit
Detected: Rubric Rating
-
Evaluation mode is explicit
Detected: Automatic Metrics, Simulation Env
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
Detected: accuracy, precision