Human Feedback Types
strongPairwise Preference
Directly usable for protocol triage.
"As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential."
HFEPX · Eval paper review
Evaluating Alignment of Behavioral Dispositions in LLMs
Amir Taubenfeld, Zorik Gekhman, Lior Nezry, Omri Feldman +7 more
Published
Feb 11, 2026
Citations
0
Trust level
Moderate
Usefulness score
50/100 (Medium)
Extraction confidence
55% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Sep 29, 2026
Should you rely on this paper?
This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.
Use this for comparison and orientation, not as your only source.
Low-signal caution for protocol decisions
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
- The abstract does not clearly describe the evaluation setup.
- The abstract does not clearly name benchmarks or metrics.
Best use
Secondary protocol comparison source
Use if you need
A concrete protocol example with enough signal to inform rater workflow design.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The abstract does not clearly describe the evaluation setup.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Weak or implicit
Validate from full paper
Usefulness for eval research
50/100
Moderate-confidence candidate
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
Abstract
As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential. In this work, we focus on behavioral dispositions: the underlying tendencies that shape responses in social contexts. We introduce STAR, a framework for studying how closely the dispositions expressed by LLMs align with those of humans. STAR builds on established psychological questionnaires, adapting their items into realistic advice-seeking scenarios, as self-report may not transfer to actual advisory behavior. Using STAR, we construct a dataset of 23k scenarios, each validated by 3 raters and annotated with preferences from 10 participants. Across 25 LLMs, we find that (1) when human consensus is high, frontier models can fail to reflect it in 15-20% of cases, and smaller models fail at substantially higher rates; (2) when humans disagree, LLM recommendations are substantially less diverse than human choices, both within individual models and even across models from different providers, potentially narrowing the range of options users are guided toward; (3) LLMs' self-reported values are poor predictors of their recommendations. To support future research we make our dataset and code publicly available.
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
missingNone explicit
Validate eval design from full paper text.
"As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential."
Quality Controls
strongAdjudication
Calibration/adjudication style controls detected.
"As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential."
Reported Metrics
missingNot extracted
No metric anchors detected.
"As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- Yes
- Feedback types
- Pairwise Preference
- Rater population
- Not reported
- Expertise required
- Coding
Evaluation details
- Evaluation modes
- None
- Agentic eval
- None
- Quality controls
- Adjudication
- Evidence quality
- Moderate
- Use this page as
- Secondary protocol comparison source
Research brief
Metadata summary
As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- As people turn to LLMs for social advice, understanding their behavior in such contexts becomes essential.
- In this work, we focus on behavioral dispositions: the underlying tendencies that shape responses in social contexts.
- We introduce STAR, a framework for studying how closely the dispositions expressed by LLMs align with those of humans.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
- 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 STAR, a framework for studying how closely the dispositions expressed by LLMs align with those of humans.
- Using STAR, we construct a dataset of 23k scenarios, each validated by 3 raters and annotated with preferences from 10 participants.
- Across 25 LLMs, we find that (1) when human consensus is high, frontier models can fail to reflect it in 15-20% of cases, and smaller models fail at substantially higher rates; (2) when humans disagree, LLM recommendations are substantially…
Why it matters for eval
- We introduce STAR, a framework for studying how closely the dispositions expressed by LLMs align with those of humans.
- Across 25 LLMs, we find that (1) when human consensus is high, frontier models can fail to reflect it in 15-20% of cases, and smaller models fail at substantially higher rates; (2) when humans disagree, LLM recommendations are substantially…
Researcher checklist
-
Human feedback protocol is explicit
Detected: Pairwise Preference
-
Evaluation mode is explicit
No clear evaluation mode extracted.
-
Quality control reporting appears
Detected: Adjudication
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
No metric terms extracted.