Challenge: Large language models struggle to utilize long contexts efficiently, resulting in a question answering problem.
Approach: They propose a method to generate a short document that contains the most relevant parts for a given context window.
Outcome: The proposed method improves the QA task by providing a short and focused VDoc to the LLM while keeping the context window full.

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PDFTriage: Question Answering over Long, Structured Documents (2024.emnlp-industry)

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Challenge: Existing approaches to document QA use a pre-retrieval step to retrieve the relevant context from documents, but this is incongruous with the user's mental model of the document.
Approach: They propose an approach called PDFTriage that enables models to retrieve the context based on either structure or content.
Outcome: The proposed approach can retrieve context based on structure or content across several classes of questions where existing retrieval-augmented LLMs fail.
Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024.emnlp-main)

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Challenge: Existing benchmarks for evaluating long-context language models employ irrelevant noise texts to artificially extend the length of test cases, diverging from the real-world scenarios of long-constituency applications.
Approach: They propose a long-context benchmark, Loong, aligning with realistic scenarios through extended multi-document question answering (QA) .
Outcome: The proposed model can scale up the context window of large language models to perform in-depth analysis of multiple long documents.
Long-Context Long-Form Question Answering for Legal Domain (2026.eacl-industry)

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Challenge: Legal documents have complex document layouts involving multiple nested sections and lengthy footnotes that make question answering challenging.
Approach: They propose a question answering system that parses document layouts while isolating sections and footnotes and linking them appropriately.
Outcome: The proposed system can parse complex document layouts while isolating sections and footnotes and linking them appropriately.
Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering (2023.findings-emnlp)

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Challenge: Long document question answering requires locating relevant paragraphs within a document to answer a question.
Approach: They propose to exploit the discourse structure commonly found in documents to create a condensed representation of the document, enabling a more comprehensive understanding and analysis of relationships between different parts.
Outcome: The proposed approach retains 99.6% of the best zero-shot approach's performance while processing only 26% of tokens used by the best approach in the information seeking evidence retrieval setup.
Can’t Remember Details in Long Documents? You Need Some R&R (2024.findings-emnlp)

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Challenge: Long-context large language models miss important information in the middle of context documents . a recent study shows that LLMs can be used for document-based QA tasks .
Approach: They propose a prompt-based method called *reprompting* and *in-context retrieval* to alleviate this effect in document-based QA.
Outcome: The proposed method improves QA accuracy on documents up to 80k tokens in length.
QuALITY: Question Answering with Long Input Texts, Yes! (2022.naacl-main)

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Challenge: Existing models for natural language understanding are limited to processing only a few hundred words at a time.
Approach: They propose a dataset with context passages in English that have an average length of 5,000 tokens.
Outcome: a new dataset with long-text comprehension questions is used to test models on long-document comprehension . the questions are validated by contributors who have read the entire passage, not just excerpts . only half of the questions can be answered by annotators working under tight time constraints .
Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models (2025.acl-long)

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Challenge: Current methods for improving large language models rely on splitting long contexts into fixed-length chunks, compromising accuracy.
Approach: They propose a method for dynamically separating and selecting chunks of long context, facilitating a more streamlined input for LLMs.
Outcome: The proposed approach outperforms baseline methods on single-hop and multi-hop question-answering benchmarks.
SEGMENT+: Long Text Processing with Short-Context Language Models (2024.emnlp-main)

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Challenge: Existing frameworks that increase context window do not guarantee robust performance across long input tasks.
Approach: They propose a framework that enables language models to handle extended inputs within limited context windows efficiently.
Outcome: The framework improves performance on long-document question-answering and Needle-in-a-Haystack tasks.
Adaptive Document Retrieval for Deep Question Answering (D18-1)

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Challenge: Existing methods for deep question answering do not understand the exact interplay between document retrieval and machine comprehension.
Approach: They propose an adaptive document retrieval model that learns the optimal document number, conditional on the size of the corpus and the query.
Outcome: The proposed model outperforms state-of-the-art methods on multiple benchmark datasets and in the context of corpora with variable sizes.
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts (2025.emnlp-industry)

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Challenge: Recent studies into effective context lengths of flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reasoning over long and complex contexts for even the largest and most impressive cadre of models.
Approach: They propose a lightweight data augmentation strategy that boosts LLM performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents.
Outcome: The proposed strategy boosts performance in long-context scenarios without degrading and altering the integrity and composition of retrieved documents.

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