README.md

metadata

pretty_name: 'MPB: Missing-Premise Reasoning Benchmark'
license: apache-2.0
language:
  - en
task_categories:
  - question-answering
  - text-generation
size_categories:
  - n<1K
tags:
  - benchmark
  - reasoning
  - missing-premise
  - uncertainty
  - abstention
  - clarification
  - text

HRB: Missing-Premise Reasoning Benchmark

HRB is the Hugging Face release of the Missing-Premise Benchmark (MPB) introduced in Ask, Condition or Abstain: Reinforcement Learning for Missing-Premise Reasoning.

The benchmark contains 274 human-verified instances designed to evaluate whether a language model can recognize when a problem is underdetermined and respond constructively—by asking for the missing premise, conditioning its answer on an unknown variable, or abstaining—instead of fabricating a definite answer.

Benchmark Overview

HRB covers mathematical, logical, and real-world word problems. Each instance is created by transforming a well-posed source problem into a fluent but underdetermined problem with a localized informational gap.

The benchmark covers six missing-premise perturbation categories:

• Relationship Removal: removes a relation required to determine the answer.
• Relationship Unquantifiable Replacement: replaces a precise relation with an indefinite one.
• Numerical Value Removal: removes a required numerical value.
• Entity Disruption: replaces a relevant entity with an unrelated but plausible entity.
• Qualifier Disruption: changes or removes a qualifier required by the original constraint.
• Condition Contraction: restricts the domain of a condition so that it no longer covers the query.

HRB is constructed from a held-out candidate pool and is not included in the 120K missing-premise instances used for ACA-RL training.

Data Construction and Verification

Source problems are drawn from Tong et al. (2023) and DeepScaleR.

Construction follows a multi-stage process:

1. A reasoning graph is built for each well-posed source problem.
2. A premise on the solution path is removed or modified.
3. The perturbed conditions are rewritten into a fluent problem statement.
4. Automated checks remove malformed or still-answerable examples.
5. Three human experts verify the original and perturbed problem pair, the missing-premise analysis, and the overall example quality.

Conceptually, each benchmark instance contains:

• the original well-posed problem;
• the perturbed missing-premise problem;
• an annotation describing the informational gap;
• the perturbation category and associated metadata.

Please refer to the Dataset Viewer for the serialized column names.

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("YOUR_ORG/HRB")

print(dataset)

for split_name, split in dataset.items():
    print(split_name, split.column_names)
    print(split[0])

This example discovers the available splits and columns rather than assuming a fixed schema.

Evaluation

A model response is assigned to one of five terminal behavior categories:

Behavior	Score	Description
Silent Hallucination	0	Produces a definite answer using fabricated or unstated information
Explicit Assumption	25	States an unsupported assumption and answers under that assumption
Abstention	50	Recognizes that the problem is underspecified and declines to give a definite answer
Conditional Formulation	75	Represents the missing information as a variable and provides a conditional answer
Active Elicitation	100	Asks for the specific missing premise needed to answer the question

The benchmark-level Behavior Score is the arithmetic mean of the per-instance scores.

The accompanying paper uses GPT-5 as the behavior judge. In a sampled agreement study, human annotations agreed with the judge on approximately 98% of GPT-5 responses and 95% of Qwen3-235B-A22B-Instruct responses.

For reproducible comparisons, evaluations should report:

• model and checkpoint;
• reasoning or direct-answer mode;
• generation parameters;
• judge model and prompt;
• overall Behavior Score;
• behavior distribution;
• per-perturbation scores.

Intended Uses

HRB is intended for:

• evaluating missing-premise recognition;
• measuring hallucination, assumption, abstention, conditioning, and clarification behavior;
• comparing uncertainty-aware reasoning methods;
• diagnosing behavior across different types of informational gaps.

HRB is not designed to measure factual knowledge, probability calibration, retrieval ability, or multi-turn clarification success.

Limitations

• The benchmark contains 274 instances and may not represent the full diversity of real-world underspecification.
• Its examples are produced through controlled structural perturbations and may not capture all organic, implicit, or semantic ambiguities.
• Active elicitation is evaluated as a single-turn textual response; the benchmark does not test whether a model can acquire the missing information through subsequent interaction or tool use.
• The Behavior Score encodes a specific preference hierarchy and should not be interpreted as a calibrated uncertainty measure.
• Automatic judging may introduce evaluator bias despite the reported human-agreement checks.

Citation

@misc{tong2026ask,
  title        = {Ask, Condition or Abstain: Reinforcement Learning for Missing-Premise Reasoning},
  author       = {Yongqi Tong and Zhenyu Zhang and Zimi Liu and Kewei Fu and
                  Mingli Song and Haofei Zhang and Junshao Zhang and Hong Zhu and
                  Jiang-Ming Yang and Xin Zhang and Jianshe Li},
  year         = {2026},
  eprint       = {2608.16554},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url          = {https://arxiv.org/abs/2608.16554}
}