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Evaluating VQA in Vision Language Models using Cooperative Principles — arXiv:2610.02878

Source: arXiv
Original: https://arxiv.org/abs/2610.02878
License (as archived): CC0-1.0
Archived: 2026-10-05T22:51:37.581Z
SHA-256 of the archived text: deb486d67957c3bb84b6517c06d4443bdf92211881b8535a17f5e37b006bd8c3
Stable URL of this page: https://metraipolis.com/documento/dc_2cf56479fa8a

How to cite

Evaluating VQA in Vision Language Models using Cooperative Principles — arXiv:2610.02878 — arXiv, https://arxiv.org/abs/2610.02878 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2cf56479fa8a (Metraipolis archive dc_2cf56479fa8a, sha256 deb486d67957c3bb84b6517c06d4443bdf92211881b8535a17f5e37b006bd8c3)

BibTeX

@misc{dc2cf56479fa8a,
  title = {Evaluating VQA in Vision Language Models using Cooperative Principles — arXiv:2610.02878},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.02878},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2cf56479fa8a; sha256 deb486d67957c3bb84b6517c06d4443bdf92211881b8535a17f5e37b006bd8c3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.02878. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

We evaluate the performance of Vision Language Models in Visual Question Answering (VQA) when questions violate Grice's maxims. To do this, we use VLMs to generate question modifiers that add non-essential, ambiguous or false information and show that in the presence of such violations, the VLMs that we evaluate (ChatGPT, Claude, Gemini and Llava) show diminished performance. Further, we empirically show the difference between how humans reason pragmatically compared to VLMs, and the difference in VLM reasoning when it resolves violations that are human-induced compared to those that are AI-generated. Finally, we show that human cognitive effort (measured through time-on-task in an experiment) is lower for resolving VLM-induced violations, but VLMs themselves perform less accurately in such cases.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.CL, nuovi lavori del giorno).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2610.02878 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-05T22:46:10.557Z dalla pagina https://arxiv.org/list/cs.CL/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).