| Challenge: | Several softwares for text evaluation are available that do not provide detailed examples. |
| Approach: | They propose a visual analysis toolkit for instance-level and corpus-level system evaluation on a wide variety of text generation tasks. |
| Outcome: | The proposed toolkit covers most common n-gram metrics and latest embedding-based metrics such as BERTScore. |
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| Challenge: | Existing MLLMs rely on commercial models such as GPT-4o for evaluations, but they are not universally accessible. |
| Approach: | They propose a task decomposition evaluation framework based on GPT-4o to automatically construct a specialized training dataset to break down the multifaceted evaluation process into simpler sub-tasks. |
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Curious Case of Language Generation Evaluation Metrics: A Cautionary Tale (2020.coling-main)
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| Challenge: | a few popular metrics are still used to evaluate language generation systems despite their known limitations. |
| Approach: | They propose to use automatic metrics to evaluate language generation systems . they show that they prefer system outputs to human-authored texts . |
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Text2Vis: A Challenging and Diverse Benchmark for Generating Multimodal Visualizations from Text (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown promise in generating visualizations from natural language, but lack of comprehensive benchmarks limits their capabilities. |
| Approach: | They propose a framework that jointly refines the textual answer and visualization code to improve GPT-4o's pass rate from 26% to 42% over direct approach. |
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VisText: A Benchmark for Semantically Rich Chart Captioning (2023.acl-long)
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| Challenge: | Current approaches for automatically generating chart captions struggle to articulate the perceptual or cognitive features that are the hallmark of charts (e.g., complex trends and patterns). |
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Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
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On the Blind Spots of Model-Based Evaluation Metrics for Text Generation (2023.acl-long)
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| Challenge: | Existing methods for text generation evaluation metrics are lacking in robustness analysis. |
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VLEU: a Method for Automatic Evaluation for Generalizability of Text-to-Image Models (2024.emnlp-main)
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INSTRUCTSCORE: Towards Explainable Text Generation Evaluation with Automatic Feedback (2023.emnlp-main)
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| Challenge: | Existing methods to evaluate the quality of language generation do not provide explicit explanation of their verdicts. |
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SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (2021.acl-demo)
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| Challenge: | despite advances in abstractive text summarization, the true performance and failure modes of modern neural models are not yet fully understood due to the black-box nature of neural models and unmanageable scale of recent datasets for manual analysis. |
| Approach: | They propose an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of models, data, and evaluation metrics associated with text summarization. |
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A Tutorial on Evaluation Metrics used in Natural Language Generation (2021.naacl-tutorials)
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| Challenge: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |
| Approach: | This tutorial presents the evolution of automatic evaluation metrics to their current state . it aims to assess the extent of scientific progress made and identify areas/components that need improvement . |
| Outcome: | This tutorial presents the evolution of automatic evaluation metrics to their current state along with emerging trends in this field. |