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Improving Attributed Long-form Question Answering with Intent Awareness

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
deep researchlong form question answeringattributed question answeringRAGsupervised fine-tuning
Abstract:

Large language models (LLMs) are increasingly being used to generate comprehensive, knowledge-intensive reports. However, while these models are trained on diverse academic papers and reports, they are not exposed to the reasoning processes and intents that guide authors in crafting these documents. We hypothesize that enhancing a model's intent awareness can significantly improve the quality of generated long-form reports. We develop and employ structured, tag-based schemes to better elicit underlying implicit intents to write or cite. We demonstrate that these extracted intents enhance both zero-shot generation capabilities in LLMs and enable the creation of high-quality synthetic data for fine-tuning smaller models. Our experiments reveal improved performance across various challenging scientific report generation tasks, with an average improvement of +2.9 and +12.3 absolute points for large and small models over baselines, respectively. Furthermore, our analysis illuminates how intent awareness enhances model citation usage and substantially improves report readability.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces an intent-aware framework for generating knowledge-intensive scientific reports, focusing on extracting and leveraging paragraph-level and citation-level intents to guide LLM generation. According to the taxonomy, this work resides in the 'Intent-Aware Report and Document Generation' leaf under 'Long-Form Answer Generation with Attribution'. Notably, this leaf contains only the original paper itself—no sibling papers are present—indicating a relatively sparse research direction within the broader taxonomy of thirteen papers across multiple branches. This positioning suggests the work occupies a distinct niche at the intersection of intent modeling and attributed long-form generation.

The taxonomy reveals neighboring research directions that contextualize this contribution. The 'Intent Modeling and Query Understanding' branch contains papers on complex query decomposition and domain-specific intent extraction, while 'Retrieval-Augmented Frameworks and Evidence Grounding' addresses evidence sourcing mechanisms. The original paper bridges these areas by applying intent reasoning specifically to the generation phase rather than query understanding or retrieval alone. The taxonomy's scope notes clarify that intent-aware generation excludes pure retrieval methods and short-form QA, positioning this work as focused on synthesizing extended, citation-grounded narratives guided by inferred authorial reasoning processes.

The contribution-level analysis examined twenty-one candidate papers across three main contributions, with no clear refutations identified. The first contribution (intent-aware writing framework) examined one candidate; the second and third contributions (inference/training strategies and empirical validation) each examined ten candidates. Among this limited search scope, no prior work was found that directly overlaps with the structured tag-based intent extraction scheme applied to scientific report generation. The absence of refutable candidates across all contributions suggests that, within the examined literature, the specific combination of paragraph and citation intent modeling for long-form scientific writing appears relatively unexplored, though the search scope remains constrained to top-K semantic matches.

Based on the limited literature search of twenty-one candidates, the work appears to occupy a novel position combining intent awareness with attributed report generation. The taxonomy structure confirms sparse coverage in this specific direction, though related intent modeling and retrieval-augmented generation methods exist in neighboring branches. The analysis does not cover exhaustive domain-specific literature or recent preprints beyond the examined candidates, leaving open questions about potential overlaps in specialized scientific writing or technical documentation domains not captured in the semantic search.

Taxonomy

Core-task Taxonomy Papers
13
3
Claimed Contributions
21
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Attributed long-form question answering with intent-aware generation. The field addresses how systems can produce comprehensive, well-grounded answers by understanding user intent, retrieving relevant evidence, generating extended responses, and providing proper attribution. The taxonomy organizes research into four main branches. Intent Modeling and Query Understanding focuses on decomposing complex queries and inferring latent user goals, with works like IntentQA[2] and Query Decomposition Reasoning[3] exploring how to parse multifaceted information needs. Retrieval-Augmented Frameworks and Evidence Grounding emphasizes methods for sourcing and anchoring claims in external knowledge, ensuring that generated content remains verifiable. Long-Form Answer Generation with Attribution tackles the synthesis of coherent, extended narratives while maintaining citation links to source material, often requiring intent-aware strategies to structure reports or documents that align with user expectations. Evaluation Benchmarks and Multimodal Understanding develops datasets and metrics that assess both textual and multimodal reasoning, as seen in works like MAVIS[6] and Multimodal Temporal Reasoning[8], broadening the scope beyond purely text-based scenarios. Several active lines of work highlight contrasting emphases and open questions. One thread explores domain-specific applications—such as Mental Health QA[7], SWE-QA[10], and Arabic QA Review[9]—where intent-aware generation must adapt to specialized vocabularies and cultural contexts. Another thread investigates multimodal and temporal reasoning, with studies like DOC2CHART[11] and Long Video Understanding[13] examining how to integrate visual or temporal cues into long-form answers. Intent Aware QA[0] sits within the Intent-Aware Report and Document Generation cluster, emphasizing structured, citation-backed narratives that respond to nuanced user intents. Compared to MuseRAG[1], which may prioritize retrieval orchestration, and Dfams[4], which could focus on factual grounding mechanisms, Intent Aware QA[0] appears to foreground the alignment between inferred intent and the organization of generated content, ensuring that lengthy answers remain both coherent and properly attributed.

Claimed Contributions

Intent-aware writing framework with paragraph and citation intents

The authors introduce a framework that incorporates two types of intents: paragraph-level writing intents (specifying the purpose of each paragraph) and sentence-level citation intents (capturing why a citation is used). These intents are represented using inline tag-based schemes with rationales to help models distinguish intent from report text.

1 retrieved paper
Intent-aware inference and training strategies for LLMs

The authors propose methods to incorporate intent awareness during both inference (by prompting models to output reports with embedded intent tags) and training (through multiple SFT variants including intent-explicit, intent-implicit, and intent-multiview approaches). These strategies improve report generation quality and enable smaller models to match larger model performance.

10 retrieved papers
Empirical validation on scientific report generation benchmarks

The authors conduct extensive experiments on three recent benchmarks (SQA-CS-V2, DeepScholar Bench, and ResearchQA) demonstrating that intent awareness consistently improves model performance. The improvements are particularly notable in citation metrics, with gains of +3.7 and +18.7 absolute points for large and small models respectively.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Intent-aware writing framework with paragraph and citation intents

The authors introduce a framework that incorporates two types of intents: paragraph-level writing intents (specifying the purpose of each paragraph) and sentence-level citation intents (capturing why a citation is used). These intents are represented using inline tag-based schemes with rationales to help models distinguish intent from report text.

Contribution

Intent-aware inference and training strategies for LLMs

The authors propose methods to incorporate intent awareness during both inference (by prompting models to output reports with embedded intent tags) and training (through multiple SFT variants including intent-explicit, intent-implicit, and intent-multiview approaches). These strategies improve report generation quality and enable smaller models to match larger model performance.

Contribution

Empirical validation on scientific report generation benchmarks

The authors conduct extensive experiments on three recent benchmarks (SQA-CS-V2, DeepScholar Bench, and ResearchQA) demonstrating that intent awareness consistently improves model performance. The improvements are particularly notable in citation metrics, with gains of +3.7 and +18.7 absolute points for large and small models respectively.

Table of Contents