{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/are-ai-detectors-good-enough-a-survey-on","title":"Are AI Detectors Good Enough? A Survey on Quality of Datasets With Machine-Generated Texts","arxiv_id":"2410.14677","date":"2024-10-18","proceeding":null,"authors":["German Gritsai","Anastasia Voznyuk","Andrey Grabovoy","Yury Chekhovich"],"abstract":"The rapid development of autoregressive Large Language Models (LLMs) has significantly improved the quality of generated texts, necessitating reliable machine-generated text detectors. A huge number of detectors and collections with AI fragments have emerged, and several detection methods even showed recognition quality up to 99.9% according to the target metrics in such collections. However, the quality of such detectors tends to drop dramatically in the wild, posing a question: Are detectors actually highly trustworthy or do their high benchmark scores come from the poor quality of evaluation datasets? In this paper, we emphasise the need for robust and qualitative methods for evaluating generated data to be secure against bias and low generalising ability of future model. We present a systematic review of datasets from competitions dedicated to AI-generated content detection and propose methods for evaluating the quality of datasets containing AI-generated fragments. In addition, we discuss the possibility of using high-quality generated data to achieve two goals: improving the training of detection models and improving the training datasets themselves. Our contribution aims to facilitate a better understanding of the dynamics between human and machine text, which will ultimately support the integrity of information in an increasingly automated world.","url_abs":"https://arxiv.org/abs/2410.14677v2","url_pdf":"https://arxiv.org/pdf/2410.14677v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"are-ai-detectors-good-enough-a-survey-on","repo_url":"https://github.com/Advacheck-OU/ai-dataset-analysing","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.14677","atlas_url":"https://app.syntology.ai/?focus=2410.14677","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14677"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Advacheck-OU/ai-dataset-analysing","reach":null}],"summary":{"ran_violates":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"d5d1269ff4552879","entry":"calc_mean_attention_on_diagonal_for_hl","repo":"Advacheck-OU/ai-dataset-analysing","repo_kind":"official","path":"src/calc_attentions.py","file_url":"https://github.com/Advacheck-OU/ai-dataset-analysing/blob/HEAD/src/calc_attentions.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d5d1269ff4552879"}},{"code_sha256_prefix":"77796da309f34960","entry":"calc_top_three_column_values","repo":"Advacheck-OU/ai-dataset-analysing","repo_kind":"official","path":"src/calc_attentions.py","file_url":"https://github.com/Advacheck-OU/ai-dataset-analysing/blob/HEAD/src/calc_attentions.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"77796da309f34960"}},{"code_sha256_prefix":"d1121bb0bed8a9a6","entry":"calc_mean_attention_on_diagonal","repo":"Advacheck-OU/ai-dataset-analysing","repo_kind":"official","path":"src/calc_attentions.py","file_url":"https://github.com/Advacheck-OU/ai-dataset-analysing/blob/HEAD/src/calc_attentions.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"d1121bb0bed8a9a6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}