Papers with data accuracy
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions (2023.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) can be used to generate text data for training and evaluating other models. |
| Approach: | They propose to use logit suppression and temperature sampling to diversify text generation but at the cost of data accuracy. |
| Outcome: | The proposed approach can increase diversity but at the cost of data accuracy. |
RuCCoD: Towards Automated ICD Coding in Russian (2025.emnlp-main)
Copied to clipboard
Alexandr Nesterov, Andrey Sakhovskiy, Ivan Sviridov, Airat Valiev, Vladimir Makharev, Petr Anokhin, Galina Zubkova, Elena Tutubalina
| Challenge: | a new dataset for clinical coding in Russian is available for download . human coders must navigate a wide array of medical terminology and time pressures . |
| Approach: | They present a new dataset for ICD coding in Russian, a language with limited biomedical resources. |
| Outcome: | The proposed model improves accuracy on an in-house EHR dataset from 2017 to 2021. |
Charting the Future: Using Chart Question-Answering for Scalable Evaluation of LLM-Driven Data Visualizations (2025.coling-main)
Copied to clipboard
| Challenge: | Existing evaluation methods rely on human judgment to assess data accuracy and visual communication, which is costly and unscalable. |
| Approach: | They propose a framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. |
| Outcome: | The proposed framework assesses data representation quality and communicative clarity of charts using two leading VQA benchmark datasets, ChartQA and PlotQA, with visualizations generated by OpenAI’s GPT-3.5 Turbo and Meta’s Llama 3.1 70B-Instruct models. |
Improve LLM-as-a-Judge Ability as a General Ability (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies focus on generative judges, but only on their judge ability. |
| Approach: | They propose a method that leverages the generative and reasoning capabilities of large language models to evaluate LLM responses across diverse scenarios, providing accurate preference signals. |
| Outcome: | The proposed model performs on RewardBench with only 2% to 40% of the data required by other training frameworks. |
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. |