Human Feedback Types
missingNone explicit
No explicit feedback protocol extracted.
"Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries."
HFEPX · Eval paper review
Abhishek Barali, Aayush Paudel
Published
Oct 22, 2026
Citations
0
Trust level
Low
Usefulness score
0/100 (Low)
Extraction confidence
15% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Oct 22, 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.
All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.
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
This paper looks adjacent to evaluation work, but not like a strong protocol reference.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries. We ask what models do when the document holds fewer items than that minimum. Under a preregistered plan, we test eight open-weight models on 558 real documents from nine annotated datasets, with six more models on hosted services as a reference. Three findings stand out. First, models treat the count as a target: a minimum shown in the schema, but not enforced, raises the share of lists longer than the true count from 34.5% to 55.4%. Second, the padding is hard to catch, because most added entries are real text taken from the document; about half of them pass a check that each entry occurs in the source. Third, common fixes help only partly: a line saying fewer items are fine removes the effect on average but misses more true items, a count in prose ("up to k") also raises over-counting, an "N/A" option under an enforced minimum still leaves half the lists padded, and re-asking with the validation error makes padding far more common. We release the code, answer lists and run records at https://github.com/AbhishekBarali/padding-to-k
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.
None explicit
No explicit feedback protocol extracted.
"Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries."
None explicit
Validate eval design from full paper text.
"Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries."
Not reported
No explicit QC controls found.
"Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries."
Not extracted
No benchmark anchors detected.
"Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries."
Not extracted
No metric anchors detected.
"Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
Extraction pipelines ask language models for JSON that follows a schema, and the schema can set a minimum number of list entries.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
No explicit human feedback protocol detected.
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.