Text2Chart31: Instruction Tuning for Chart Generation with Automatic Feedback (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing datasets do not cover full range of chart types, such as 3D, volumetric, and gridded charts. |
| Approach: | They propose a hierarchical pipeline and a new dataset for chart generation that leverages the relationships within rich datasets. |
| Outcome: | The proposed method outperforms open-source models and is comparable to state-of-the-art proprietary models in data visualization tasks. |
Similar Papers
ChartInstruct: Instruction Tuning for Chart Comprehension and Reasoning (2024.findings-acl)
Copied to clipboard
| Challenge: | Charts provide visual representations of data and are used for analyzing information, addressing queries, and conveying insights to others. |
| Approach: | They propose a chart-specific vision-language Instruction-following dataset with 191K instructions and a pipeline model that extracts chart data tables and inputs them into a LLM. |
| Outcome: | The proposed model can solve a wide range of chart-related tasks, achieving state-of-the-art results on four tasks. |
C2: Scalable Auto-Feedback for LLM-based Chart Generation (2025.naacl-long)
Copied to clipboard
Woosung Koh, Janghan Yoon, MinHyung Lee, Youngjin Song, Jaegwan Cho, Jaehyun Kang, Taehyeon Kim, Se-Young Yun, Youngjae Yu, Bongshin Lee
| Challenge: | generating high-quality charts with Large Language Models presents significant challenges due to limited data and the high cost of curation. |
| Approach: | They propose a referencefree automatic feedback generator to generate high-quality charts with Large Language Models. |
| Outcome: | The proposed framework outperforms baselines and shows that it significantly improves data diversity. |
VisCoder: Fine-Tuning LLMs for Executable Python Visualization Code Generation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing instruction-tuning datasets lack execution-grounded supervision and offer limited support for iterative code correction. |
| Approach: | They propose a large-scale instruction tuning dataset for Python-based visualization and self-correction. |
| Outcome: | The proposed dataset outperforms strong open-source baselines and proprietary models like GPT-4o-mini. |
Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation (2025.emnlp-main)
Copied to clipboard
Tianhao Niu, Yiming Cui, Baoxin Wang, Xiao Xu, Xin Yao, Qingfu Zhu, Dayong Wu, Shijin Wang, Wanxiang Che
| Challenge: | Existing Chart2code-related training datasets suffer from limited scale, limited type coverage, and inadequate complexity. |
| Approach: | They propose to synthesize chart2code-related training datasets using web plotting code and chart images to address these challenges. |
| Outcome: | The proposed dataset exhibits the greatest diversity and higher complexity compared to other open-source Chart2code related datasets. |
LongForm: Effective Instruction Tuning with Reverse Instructions (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Prior work on instruction tuning relies on expensive human annotation and crowd-sourced datasets with alignment issues. |
| Approach: | They propose a method to generate instructions via LLMs from human-written corpus examples using reverse instructions. |
| Outcome: | The proposed method outperforms larger language models without instruction tuning on tasks such as story/recipe generation and long-form question answering. |
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai (2024.acl-srw)
Copied to clipboard
| Challenge: | Xue et al., 2024) have demonstrated that large language models can perform at human level across multitudes of tasks and domains. |
| Approach: | They propose a seed-free framework for generating synthetic instruction-tuning data that incorporates fluency, diversity, and cultural context. |
| Outcome: | The proposed framework achieves competitive performance using only 5,000 instructions compared to state-of-the-art Thai LLMs trained on hundreds of thousands of instructions. |
Demystifying Instruction Mixing for Fine-tuning Large Language Models (2024.acl-srw)
Copied to clipboard
| Challenge: | Instruction tuning is effective for aligning large language models with human instructions, but the procedure to optimizing the mixing of instruction datasets is still unclear. |
| Approach: | They categorize instructions into three primary types: NLP downstream tasks, coding, and general chat. |
| Outcome: | The proposed method improves performance of large language models (LLMs) but it is difficult to combine different instruction datasets to optimize overall performance. |
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement (2024.findings-acl)
Copied to clipboard
Nicholas Lee, Thanakul Wattanawong, Sehoon Kim, Karttikeya Mangalam, Sheng Shen, Gopala Anumanchipalli, Michael Mahoney, Kurt Keutzer, Amir Gholami
| Challenge: | Pretrained large language models are currently state-of-the-art for solving most tasks . however, many of them are in the low-data regime, making fine-tuning challenging . a new data augmentation strategy uses a teacher LLM to augment a small seed dataset . |
| Approach: | They propose a targeted and iterative data augmentation strategy that augments a teacher LLM to fine-tune a small seed dataset by adding additional data. |
| Outcome: | The proposed approach outperforms fine-tuning and other data augmentation strategies on a small seed dataset. |
Dynamics of Instruction Fine-Tuning for Chinese Large Language Models (2025.coling-main)
Copied to clipboard
| Challenge: | Instruction tuning is a burgeoning method to elicit the general intelligence of Large Language Models. |
| Approach: | They investigate the effects of data quantity, model size, and data construction methods on instruction tuning for Chinese LLMs. |
| Outcome: | The proposed model includes over 40,000 high-quality instruction instances covering ten underlying abilities. |
Doc2Chart: Intent-Driven Zero-Shot Chart Generation from Documents (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models have demonstrated strong capabilities in transforming text descriptions or tables to data visualizations . however, it is not straightforward to apply these methods directly for a more real-world use case of visualizing data from long documents . |
| Approach: | They propose an unsupervised method for generating intent-based charts from documents . they propose an attribution-based metric that uses a structured textual representation of charts . |
| Outcome: | The proposed method outperforms baselines in terms of chart data accuracy and chart type over baselines. |