| Challenge: | Unlike existing tools, our system addresses the ambiguity of vague, multi-line queries, setting a new benchmark in data storytelling by tackling complexities no existing system comprehensively handles. |
| Approach: | They propose a system that processes and interprets vague, open-ended, and multi-line complex queries, transforming them into coherent, actionable data stories. |
| Outcome: | The proposed system processes and interprets vague, open-ended, and multi-line complex queries, transforming them into coherent, actionable data stories. |
Similar Papers
DataNarrative: Automated Data-Driven Storytelling with Visualizations and Texts (2024.emnlp-main)
Copied to clipboard
| Challenge: | Data-driven storytelling uses visual aids and visualizations to convey insights. |
| Approach: | They propose a task for data story generation using large language models and a benchmark containing 1,449 stories from diverse sources. |
| Outcome: | The proposed framework outperforms non-agentic counterparts in both model-based and human evaluations, but also reveals unique challenges in data story generation. |
Storytelling from Structured Data and Knowledge Graphs : An NLG Perspective (P19-4)
Copied to clipboard
| Challenge: | tutorial aims to explain the basic concepts of translating structured data into natural language . Various solutions for structured data translation will be discussed . |
| Approach: | tutorial aims to cover foundational, methodological, and system development aspects of translating structured data into natural language . Various solutions starting from traditional rule based/heuristic driven and modern data-driven will be discussed . |
| Outcome: | The tutorial aims to convey challenges and nuances in structured data translation, data representation techniques, and domain adaptable solutions for translation of the data into natural language form. |
An LLM-Based Approach for Insight Generation in Data Analysis (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing approaches to generate insightful data from databases are time-consuming and resource-intensive. |
| Approach: | They propose a method that leverages Large Language Models to automatically generate textual insights from databases. |
| Outcome: | The proposed approach generates more insightful insights than other approaches while maintaining correctness. |
StoryCoder: Narrative Reformulation for Structured Reasoning in LLM Code Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches augment reasoning steps or inject specific structure into how models think, but leave scattered problem conditions unchanged. |
| Approach: | They propose a narrative reformulation framework that transforms code generation questions into coherent natural language narratives. |
| Outcome: | The proposed framework improves the performance of 11 code generation models on HumanEval, LiveCodeBench, and CodeForces. |
DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text (2024.findings-naacl)
Copied to clipboard
| Challenge: | Retrievalaugmented LLMs have been used to ground LLM in external knowledge . a gap exists in the current landscape regarding the effectiveness of grounding LLM on heterogeneous knowledge sources. |
| Approach: | They propose a model that uses symbolic language to generate symbolic queries . they use a dataset that is generated using predefined reasoning chains and human annotation . |
| Outcome: | The proposed model outperforms previous approaches by a significant margin in QA tasks over text. |
Chart Question Answering from Real-World Analytical Narratives (2025.acl-srw)
Copied to clipboard
| Challenge: | a dataset for chart question answering is constructed from visualization notebooks . data visualizations are an essential modality for communicating complex information about data. |
| Approach: | They propose a dataset for chart question answering constructed from visualization notebooks . they use real-world, multi-view charts paired with natural language questions . |
| Outcome: | The proposed dataset is constructed from student-authored visualization notebooks . it features real-world, multi-view charts paired with natural language questions . initial evaluations highlight significant performance gaps . |
Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Large language models (LLMs) excel in generating coherent texts, but their ability to comprehend the author’s thoughts remains uncertain. |
| Approach: | They conduct a comprehensive survey of narrative understanding tasks, examining their key features, definitions, taxonomy, associated datasets, evaluation metrics, and limitations. |
| Outcome: | The proposed framework could be extended to address novel narrative understanding tasks. |
Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
Copied to clipboard
| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to generate SQL from natural language are still making many mistakes . a new interaction mechanism allows users to edit a step-by-step explanation of a query to fix errors. |
| Approach: | They propose a mechanism that allows users to edit a step-by-step explanation of a query to fix errors. |
| Outcome: | The proposed approach can achieve better performance than multiple SOTA approaches on multiple datasets and 24 participants. |
LENS: LLM-Enabled Narrative Synthesis for Mental Health by Aligning Multimodal Sensing with Language Models (2026.acl-long)
Copied to clipboard
Wenxuan Xu, Arvind Pillai, Subigya Nepal, Amanda C. Collins, Daniel M Mackin, Michael V. Heinz, Tess Z Griffin, Nicholas C. Jacobson, Andrew Campbell
| Challenge: | Current LLMs cannot natively ingest long-duration sensor streams and paired sensor–text datasets are scarce. |
| Approach: | They propose a framework that aligns multimodal sensing data with language models to generate clinically grounded mental-health narratives. |
| Outcome: | The proposed framework outperforms baselines on NLP metrics and task-specific measures of symptom severity and clinically meaningful narratives. |