# LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering Source: [LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering]() Collection index: [llms.txt]() Data updated: 2026-03-03T05:00:48.459Z Authors: Rafid Ishrak Jahan, Fahmid Shahriar Iqbal, Sagnik Ray Choudhury Published: 2026-02-27T02:14:15.000Z ## Abstract Long-form question answering (LFQA) demands nuanced evaluation of multi-sentence explanatory responses, yet existing metrics often fail to reflect human judgment. We present LFQA-HP-1M, a large-scale dataset comprising 1.3M human pairwise preference annotations for LFQA. We propose nine rubrics for answer quality evaluation, and show that simple linear models based on these features perform comparably to state-of-the-art LLM evaluators. We further examine transitivity consistency, positional bias, and verbosity biases in LLM evaluators and demonstrate their vulnerability to adversarial perturbations. Overall, this work provides one of the largest public LFQA preference datasets and a rubric-driven framework for transparent and reliable evaluation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()