HFEPX · Eval paper review
Obed Junias, Maria Leonor Pacheco
Published
Aug 13, 2026
Citations
0
Trust level
Low
Usefulness score
5/100 (Low)
Extraction confidence
45% (Low)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Aug 13, 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
A benchmark-and-metrics comparison anchor.
What to verify
Validate the evaluation procedure and quality controls in the full paper before operational use.
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
Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the individual atoms correctly. We study compound options connected by AND, OR, and NEITHER/NOR, introducing a framework that decomposes each option into atomic answers and scores contrastive hypotheses about each one, so the model never sees a compound option. An operator-constrained integer linear program then composes the calibrated scores into a single prediction. We evaluate on LOGICAL-COMMONSENSEQA and introduce LOGICAL-SATA, a reading-comprehension benchmark derived from SATA-Bench. Our framework improves Macro-F1 from 48.3 to 77.0 on the human-validated LOGICAL-COMMONSENSEQA split and from 47.0 to 75.6 on LOGICAL-SATA, with the largest gains on NEITHER/NOR.
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.
"Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the individual atoms correctly."
Automatic Metrics
Includes extracted eval setup.
"Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the individual atoms correctly."
Not reported
No explicit QC controls found.
"Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the individual atoms correctly."
CommonsenseQA, Sata Bench
Useful for quick benchmark comparison.
"We evaluate on LOGICAL-COMMONSENSEQA and introduce LOGICAL-SATA, a reading-comprehension benchmark derived from SATA-Bench."
F1, F1 macro
Useful for evaluation criteria comparison.
"Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the individual atoms correctly."
Large language models often fail when answer options require combining atomic judgments under explicit logical operators, even when they judge the individual atoms correctly.
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
Detected: Automatic Metrics
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present
Detected: CommonsenseQA, Sata-Bench
Metric reporting is present
Detected: f1, f1 macro
