Human Feedback Types
partialPairwise Preference
Directly usable for protocol triage.
"Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences."
HFEPX · Eval paper review
Haokai Zhao, Yunze Xiao, Weihao Xuan, Flora Salim +2 more
Published
Aug 12, 2026
Citations
0
Trust level
Low
Usefulness score
40/100 (Low)
Extraction confidence
45% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Aug 12, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this as background context only. Do not make protocol decisions from this page alone.
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
Best use
Background context only
Use if you need
Background context only.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The available metadata is too thin to trust this as a primary source.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences. Sycophancy, a well-documented by-product of alignment, causes the model to over-agree with the user regardless of factual and objective information. However, existing group alignment methods and evaluations focus only on how closely the model matches the group's opinions, overlooking the induced change in sycophantic behaviour. To bridge this gap, we introduce \textbf{G}roup \textbf{A}lignment-induced \textbf{S}ycophancy (GAS) and systematically evaluate alignment across 3 methods, 4 models and 13 demographic groups, on both the intended gain in opinion alignment and the unintended shift in sycophancy. We find that gain and shift are non-uniform across groups: under an identical budget, some groups receive larger gains in opinion alignment than others, and the induced sycophancy shift forms a group-specific profile rather than a single-dimensional change. These results suggest that group alignment should be reported as a two-sided, multi-dimensional profile rather than a single fit score that accounts for per-group differences when adapting LLMs to diverse populations.
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.
Pairwise Preference
Directly usable for protocol triage.
"Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences."
None explicit
Validate eval design from full paper text.
"Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences."
Not reported
No explicit QC controls found.
"Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences."
Not extracted
No benchmark anchors detected.
"Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences."
Not extracted
No metric anchors detected.
"Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
Group alignment adapts a language model to a demographic group to produce responses that reflect the group's opinions, values, and preferences.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
Detected: Pairwise Preference
Evaluation mode is explicit
No clear evaluation mode extracted.
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
No metric terms extracted.