Automatic Evaluation for Text-to-image Generation: Task-decomposed Framework, Distilled Training, and Meta-evaluation Benchmark (2025.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework outperforms the current state-of-the-art GPT-4o evaluation framework with over 4.6% improvement in Spearman and Kendall correlations with human judgments. |
Similar Papers
GPTScore: Evaluate as You Desire (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing evaluation frameworks for text generation are not adequate to assess the quality of the generated outputs. |
| Approach: | They propose a framework that utilizes emergent abilities of generative pre-trained models to evaluate generated texts. |
| Outcome: | The proposed evaluation framework can achieve what one desires to evaluate for texts simply by natural language instructions. |
Holistic Evaluation for Interleaved Text-and-Image Generation (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing evaluation benchmarks do not support arbitrarily interleaved images and text for both inputs and outputs. |
| Approach: | They propose to use a benchmark to evaluate interleaved text-and-image generation . they define five evaluation aspects for InterleavatedEval, a reference-free metric . |
| Outcome: | The proposed benchmarks cover a limited number of domains and use cases and lack comparableity-based metrics. |
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape . established automatic evaluation metrics are poor surrogates, correlating weakly with human judgement. |
| Approach: | They propose to use both automatic and human evaluation to evaluate generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction. |
| Outcome: | The proposed model outperforms many popular models according to human reviewers on the majority of metrics, while scoring much worse when using classic automatic evaluation metrics. |
AcT2I: Evaluating and Improving Action Depiction in Text-to-Image Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Text-to-Image (T2I) models have been successful in generating images from textual descriptions, but they struggle to capture nuanced and implicit attributes inherent in action depiction. |
| Approach: | They propose a benchmark to evaluate the performance of T2I models in generating images from action-centric prompts. |
| Outcome: | The proposed model achieves an increase of 72% on AcT2I. |
VIEScore: Towards Explainable Metrics for Conditional Image Synthesis Evaluation (2024.acl-long)
Copied to clipboard
| Challenge: | Existing metrics for conditional image generation are opaque and lack explainability . evaluators of these metrics have limited ability to evaluate image synthesis tasks . |
| Approach: | They propose a Visual Instruction-guided Explainable metric for evaluating conditional image models. |
| Outcome: | The proposed model achieves a high Spearman correlation with human evaluations, but is weaker than GPT-4o and GPT-v in evaluating synthetic images. |
SESCORE2: Learning Text Generation Evaluation via Synthesizing Realistic Mistakes (2023.acl-long)
Copied to clipboard
| Challenge: | Existing learned metrics perform unsatisfactory across text generation tasks or require human annotations for training on specific tasks. |
| Approach: | They propose a self-supervised approach to train a model-based metric for text generation evaluation using sentences retrieved from a corpus. |
| Outcome: | The proposed model outperforms all prior unsupervised metrics on four text generation evaluation benchmarks, with an average Kendall improvement of 0.158. |
RefVNLI: Towards Scalable Evaluation of Subject-driven Text-to-image Generation (2025.findings-emnlp)
Copied to clipboard
Aviv Slobodkin, Hagai Taitelbaum, Yonatan Bitton, Brian Gordon, Michal Sokolik, Nitzan Bitton Guetta, Almog Gueta, Royi Rassin, Dani Lischinski, Idan Szpektor
| Challenge: | Existing methods assess only one aspect of the task, misalign with human judgments or rely on costly API-based evaluation. |
| Approach: | RefVNLI evaluates textual alignment and subject preservation in a single run. |
| Outcome: | RefVNLI outperforms or matches existing baselines across multiple benchmarks and subject categories. |
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering (2023.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. |
| Approach: | They propose a metric that evaluates natural language generation tasks as an instruction-style question answering task and utilizes instruction-tuned pre-trained language models without training on evaluation datasets. |
| Outcome: | The proposed metric achieves state-of-the-art performance in untrained metrics for evaluating text summarization and dialogue generation, which exhibits strong dimension-level / task-level generalization ability and interpretability. |
VizSeq: a visual analysis toolkit for text generation tasks (D19-3)
Copied to clipboard
| 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. |
MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
Copied to clipboard
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation. |
| Approach: | They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field. |
| Outcome: | The proposed framework evaluates generative models on 16 NLP datasets across 70 typologically diverse languages and compares them to state-of-the-art non-autoregressive models. |