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ARXIV:2603.15253 · IMAGE CAPTIONING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15253IMAGE CAPTIONINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models.
Opportunity summary
Pain HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models. Beyond evaluation, effective hallucination detection is also essential for curating high-quality image-caption pairs used to train…
Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misrepresent the image. Beyond evaluation, effective hallucination detection is also essential…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our project page is available at https://dahlian00.github.io/HalDec-Bench-Page/.
Image Captioning moved forward this cycle; last verified April 2026. Public score 7.0/10.
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HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models.
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10.48550/arXiv.2603.15253HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models.
Abstract
Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misrepresent the image. Beyond evaluation, effective hallucination detection is also essential for curating high-quality image-caption pairs used to train VLMs. However, the generalizability of VLMs as hallucination detectors across different captioning models and hallucination types remains unclear due to the lack of a comprehensive benchmark. In this work, we introduce HalDec-Bench, a benchmark designed to evaluate hallucination detectors in a principled and interpretable manner. HalDec-Bench contains captions generated by diverse VLMs together with human annotations indicating the presence of hallucinations, detailed hallucination-type categories, and segment-level labels. The benchmark provides tasks with a wide range of difficulty levels and reveals performance differences across models that are not visible in existing multimodal reasoning or alignment benchmarks. Our analysis further uncovers two key findings. First, detectors tend to recognize sentences appearing at the beginning of a response as correct, regardless of their actual correctness. Second, our experiments suggest that dataset noise can be substantially reduced by using strong VLMs as filters while employing recent VLMs as caption generators. Our project page is available at https://dahlian00.github.io/HalDec-Bench-Page/.
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
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Dimensions overall score 7.0
PROBLEM
HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models. Beyond evaluation, effective hallucination detection is also essential for curating high-quality image-caption pairs used to tra...
METHOD
Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misrepresent the image. Beyond evaluation, effective hallucination detection is also essential for curating h...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our project page is available at https://dahlian00.github.io/HalDec-Bench-Page/.
WHY NOW
Image Captioning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models. Beyond evaluation, effective hallucination detection is also essential for curating high-quality image-caption pairs used to train VLMs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Hallucination detection in captions (HalDec) assesses a vision-language model's ability to correctly align image content with text by identifying errors in captions that misrepresent the image. Beyond evaluation, effective hallucination detection is also essential for curating high-quality image-caption pairs used to train VLMs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Our project page is available at https://dahlian00.github.io/HalDec-Bench-Page/.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Image Captioning moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
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HalDec-Bench is a comprehensive benchmark for evaluating hallucination detectors in image captioning, enhancing the quality of vision-language models.
Segment
Image Captioning
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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partial
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