Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking, Fine-Grained Diagnosis, and Cost-Aware Routing

• URL: http://arxiv.org/abs/2608.24112v1
• Date: Tue, 25 Aug 2026 06:20:18 GMT
• Title: Scalable Question-Centric Text-to-Image Evaluation: Reliable Ranking, Fine-Grained Diagnosis, and Cost-Aware Routing
• Abstract summary: QC-T2I-Bench is a question-centric framework that converts open prompts into attributed atomic questions.<n>We evaluate multiple open-source T2I models on English and Chinese prompts.
• Score: 9.025294637245661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern text-to-image (T2I) models often have similar total scores but different strengths, making practical selection difficult. Fine-grained benchmarks decompose prompts into questions, yet often return them to prompt scores and fixed categories, weakening attribution and ignoring complexity. Related requirements are also scored separately or as one total, obscuring basic versus compositional failure. We present QC-T2I-Bench, a question-centric framework that converts open prompts into attributed atomic questions and organizes their dependencies with Davidsonian Scene Graphs (DSGs). We use hierarchy-constrained question aggregation to exclude downstream questions after a prerequisite fails and to prevent simple and complex prompts from receiving the same total weight. We then use the DSG structure to measure joint success within prompts and compare repeated entities across prompts, separating basic realization failures from failures under additional requirements. We evaluate multiple open-source T2I models on English and Chinese prompts. The resulting question-level evidence supports reliable ranking and fine-grained diagnosis: joint completion falls from 80.7\% for components with two capabilities to 37.2\% for those with seven or more. Finally, we reuse the same records for training-free routing; our cost-aware router matches ERNIE's 89.51-point estimate with 21.3\% less GPU-s/MP.

Related papers

• Arena-T2I Hard: Benchmarking and Improving Faithfulness with Dependency-Aware Checklist [69.81453995715673]
  Existing faithfulness benchmarks rely on simple atomic instructions.<n>We introduce Arena-T2I Hard, a 310-prompt stress benchmark drawn from real arena T2I logs.<n>We propose a dependency-aware checklist reward that decomposes each prompt into a DAG of yes/no questions and zeroes descendants of failed parents.
  arXiv  Detail & Related papers  (2026-06-30T14:17:05Z)
• The Reasoning Bottleneck in Graph-RAG: Structured Prompting and Context Compression for Multi-Hop QA [4.190399245544758]
  Graph-RAG systems achieve strong multi-hop question answering by indexing documents into knowledge graphs, but strong retrieval does not guarantee strong answers.<n>We show that 77% to 91% of questions have the gold answer in the retrieved context, yet accuracy is only 35% to 78%, and 73% to 84% of errors are reasoning failures.<n>We propose two augmentations: (i) SPARQL chain-of-thought prompting, which decomposes questions into triple-pattern queries aligned with the entity-relationship context, and (ii) graph-walk compression.
  arXiv  Detail & Related papers  (2026-03-14T17:29:13Z)
• MDER-DR: Multi-Hop Question Answering with Entity-Centric Summaries [2.756584457554517]
  Map-Disambiguate-Enrich-Reduce (MDER) generates context-derived triple descriptions and integrates them with entity-level summaries.<n>Decompose-Resolve (DR) decomposes user queries into resolvable triples and grounds them in the Knowledge Graph.<n>MDER and DR form an LLM-driven QA pipeline that is robust to sparse, incomplete, and complex relational data.
  arXiv  Detail & Related papers  (2026-03-11T18:38:44Z)
• GRADE: Generating multi-hop QA and fine-gRAined Difficulty matrix for RAG Evaluation [5.002953635224383]
  Retrieval-Augmented Generation (RAG) systems are widely adopted in knowledge-intensive NLP tasks.<n>Current evaluations often overlook the structural complexity and multi-step reasoning required in real-world scenarios.<n>We propose textscGRADE, a novel evaluation framework that models task difficulty along two dimensions.
  arXiv  Detail & Related papers  (2025-08-23T11:26:41Z)
• Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation [64.64849950642619]
  We develop an evaluation framework inspired by formal semantics for evaluating text-to-image models. We show that Davidsonian Scene Graph (DSG) produces atomic and unique questions organized in dependency graphs. We also present DSG-1k, an open-sourced evaluation benchmark that includes 1,060 prompts.
  arXiv  Detail & Related papers  (2023-10-27T16:20:10Z)
• Successive Prompting for Decomposing Complex Questions [50.00659445976735]
  Recent works leverage the capabilities of large language models (LMs) to perform complex question answering in a few-shot setting. We introduce Successive Prompting'', where we iteratively break down a complex task into a simple task, solve it, and then repeat the process until we get the final solution. Our best model (with successive prompting) achieves an improvement of 5% absolute F1 on a few-shot version of the DROP dataset.
  arXiv  Detail & Related papers  (2022-12-08T06:03:38Z)
• QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance [54.48031346496593]
  We propose $textbfQRelScore$, a context-aware evaluation metric for $underlinetextbfRel$evance evaluation metric. Based on off-the-shelf language models such as BERT and GPT2, QRelScore employs both word-level hierarchical matching and sentence-level prompt-based generation. Compared with existing metrics, our experiments demonstrate that QRelScore is able to achieve a higher correlation with human judgments while being much more robust to adversarial samples.
  arXiv  Detail & Related papers  (2022-04-29T07:39:53Z)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)
• Robust Question Answering Through Sub-part Alignment [53.94003466761305]
  We model question answering as an alignment problem. We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets.
  arXiv  Detail & Related papers  (2020-04-30T09:10:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.