Inferential Question Answering

2026-02-01

AI Summary

This work addresses the challenge that existing question answering systems struggle with questions whose answers are not explicitly stated but must be inferred from contextual clues. It formally defines and systematically investigates the task of "reasoning-based question answering," introducing QUIT—a high-quality dataset comprising 7,401 questions and 2.4 million paragraphs, annotated with multi-level relevance labels through a human-in-the-loop curation process. The proposed approach integrates large language model–based answer plausibility scoring, human verification, a retrieval–reranking pipeline, and a reasoning-oriented reader fine-tuning strategy. Experimental results reveal that conventional QA methods perform poorly on this task: retrieval is ineffective, reranking yields limited gains, and fine-tuning remains unstable—even specialized reasoning models fail to outperform general-purpose smaller models—highlighting a significant gap in current systems’ capacity for complex inference.

Technology Category

• Natural Language Processing: Question Answering
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web question answering
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Community question answering

Abstract

Despite extensive research on a wide range of question answering (QA) systems, most existing work focuses on answer containment-i.e., assuming that answers can be directly extracted and/or generated from documents in the corpus. However, some questions require inference, i.e., deriving answers that are not explicitly stated but can be inferred from the available information. We introduce Inferential QA -- a new task that challenges models to infer answers from answer-supporting passages which provide only clues. To study this problem, we construct QUIT (QUestions requiring Inference from Texts) dataset, comprising 7,401 questions and 2.4M passages built from high-convergence human- and machine-authored hints, labeled across three relevance levels using LLM-based answerability and human verification. Through comprehensive evaluation of retrievers, rerankers, and LLM-based readers, we show that methods effective on traditional QA tasks struggle in inferential QA: retrievers underperform, rerankers offer limited gains, and fine-tuning provides inconsistent improvements. Even reasoning-oriented LLMs fail to outperform smaller general-purpose models. These findings reveal that current QA pipelines are not yet ready for inference-based reasoning. Inferential QA thus establishes a new class of QA tasks that move towards understanding and reasoning from indirect textual evidence.

Problem

• Inferential QA
• question answering
• textual inference
• reasoning
• answerability

Innovation

• Inferential QA
• reasoning
• QUIT dataset
• answer inference
• indirect evidence