# Knowing What's Missing: Assessing Information Sufficiency in Question Answering

arXiv: https://arxiv.org/abs/2512.06476 · HTML: https://mlsanity.com/paper/2512.06476/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 37 references that are in the map.
- First posted on arXiv 6 Dec 2025.

## Builds on (most cited first, 23 of 23)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps](https://mlsanity.com/paper/2011.01060/) (2020)
- [MuSiQue: Multihop Questions via Single-hop Question Composition](https://mlsanity.com/paper/2108.00573/) (2021)
- [AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions](https://mlsanity.com/paper/2506.09038/) (2025)
- [FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"](https://mlsanity.com/paper/2410.03727/) (2024)
- [Sufficient Context: A New Lens on Retrieval Augmented Generation Systems](https://mlsanity.com/paper/2411.06037/) (2024)
- [Do LLMs Know When to NOT Answer? Investigating Abstention Abilities of Large Language Models](https://mlsanity.com/paper/2407.16221/) (2024)
- [Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations](https://mlsanity.com/paper/2404.10960/) (2024)
- [(QA)2: Question Answering with Questionable Assumptions](https://mlsanity.com/paper/2212.10003/) (2022)
- [A Survey of Large Language Model Agents for Question Answering](https://mlsanity.com/paper/2503.19213/) (2025)
- [Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities](https://mlsanity.com/paper/2505.20099/) (2025)
- [Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations](https://mlsanity.com/paper/2402.15062/) (2024)
- [MAQA: Evaluating Uncertainty Quantification in LLMs Regarding Data Uncertainty](https://mlsanity.com/paper/2408.06816/) (2024)
- [LLMs Can Generate a Better Answer by Aggregating Their Own Responses](https://mlsanity.com/paper/2503.04104/) (2025)
- [Simulating Bandit Learning from User Feedback for Extractive Question Answering](https://mlsanity.com/paper/2203.10079/) (2022)
- [Training a Utility-based Retriever Through Shared Context Attribution for Retrieval-Augmented Language Models](https://mlsanity.com/paper/2504.00573/) (2025)
- [AbsenceBench: Language Models Can't Tell What's Missing](https://mlsanity.com/paper/2506.11440/) (2025)
- [I Could've Asked That: Reformulating Unanswerable Questions](https://mlsanity.com/paper/2407.17469/) (2024)
- [Faithfulness-Aware Uncertainty Quantification for Fact-Checking the Output of Retrieval Augmented Generation](https://mlsanity.com/paper/2505.21072/) (2025)
- [Question: How do Large Language Models perform on the Question Answering tasks? Answer:](https://mlsanity.com/paper/2412.12893/) (2024)

## Built on by (most cited first, 3 of 3)

- [The Text Uncanny Valley: Non-Monotonic Performance Degradation in LLM Information Retrieval](https://mlsanity.com/paper/2605.07186/) (2026)
- [SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature](https://mlsanity.com/paper/2608.30214/) (2026)
- [PolERo: Studying Political Evasion in Romanian](https://mlsanity.com/paper/2609.02391/) (2026)

## Abstract (arXiv)

Determining whether a provided context contains sufficient information to answer a question is a critical challenge for building reliable question-answering systems. While simple prompting strategies have shown success on factual questions, they frequently fail on inferential ones that require reasoning beyond direct text extraction. We hypothesize that asking a model to first reason about what specific information is missing provides a more reliable, implicit signal for assessing overall sufficiency. To this end, we propose a structured Identify-then-Verify framework for robust sufficiency modeling. Our method first generates multiple hypotheses about missing information and establishes a semantic consensus. It then performs a critical verification step, forcing the model to re-examine the source text to confirm whether this information is truly absent. We evaluate our method against established baselines across diverse multi-hop and factual QA datasets. The results demonstrate that by guiding the model to justify its claims about missing information, our framework produces more accurate sufficiency judgments while clearly articulating any information gaps.