HFEPX · Eval paper review
Qianyun Guo, Yibo Li, Yue Liu, Bryan Hooi
Published
Mar 4, 2026
Citations
0
Trust level
Moderate
Usefulness score
67/100 (Medium)
Extraction confidence
70% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Aug 31, 2026
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.
Best use
Secondary protocol comparison source
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.
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactions. However, assessing how well LLMs can follow these preferences in natural, long-term situations remains underexplored. This work proposes RealPref, a benchmark for evaluating natural preference-following in personalized user-LLM interactions. RealPref features 100 synthetic user profiles, 1300 personalized preferences, 4 types of preference expression (from explicit to implicit), and long-horizon interaction histories. It explored three types of test tasks (multiple-choice, true-or-false, and open-ended), with granular rubrics for LLM-as-a-judge evaluation. Results indicate that LLM performance drops significantly as context length grows and preference expression becomes more implicit, and that generalizing user preference understanding to unseen scenarios poses further challenges. RealPref and these findings provide a foundation for future research to develop user-aware LLM assistants that better adapt to individual needs.
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, Rubric Rating
Directly usable for protocol triage.
"Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactions."
Llm As Judge
Includes extracted eval setup.
"Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactions."
Not reported
No explicit QC controls found.
"Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactions."
Not extracted
No benchmark anchors detected.
"Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactions."
Context length
Useful for evaluation criteria comparison.
"Results indicate that LLM performance drops significantly as context length grows and preference expression becomes more implicit, and that generalizing user preference understanding to unseen scenarios poses further challenges."
No benchmark or dataset names were extracted from the available abstract.
Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactions.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
Detected: Pairwise Preference, Rubric Rating
Evaluation mode is explicit
Detected: Llm As Judge
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: context length
