{"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/gemba-mqm-detecting-translation-quality-error","title":"GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4","arxiv_id":"2310.13988","date":"2023-10-21","proceeding":null,"authors":["Tom Kocmi","Christian Federmann"],"abstract":"This paper introduces GEMBA-MQM, a GPT-based evaluation metric designed to detect translation quality errors, specifically for the quality estimation setting without the need for human reference translations. Based on the power of large language models (LLM), GEMBA-MQM employs a fixed three-shot prompting technique, querying the GPT-4 model to mark error quality spans. Compared to previous works, our method has language-agnostic prompts, thus avoiding the need for manual prompt preparation for new languages. While preliminary results indicate that GEMBA-MQM achieves state-of-the-art accuracy for system ranking, we advise caution when using it in academic works to demonstrate improvements over other methods due to its dependence on the proprietary, black-box GPT model.","url_abs":"https://arxiv.org/abs/2310.13988v1","url_pdf":"https://arxiv.org/pdf/2310.13988v1.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":"gemba-mqm-detecting-translation-quality-error","repo_url":"https://github.com/microsofttranslator/gemba","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"CC-BY-SA-4.0"}}],"tasks":[{"task_slug":"translation","task_name":"Translation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"discriminative-fine-tuning","method_name":"Discriminative Fine-Tuning"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt","method_name":"GPT"},{"method_slug":"gpt-4","method_name":"GPT-4"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2310.13988","atlas_url":"https://app.syntology.ai/?focus=2310.13988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13988"}},"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/microsofttranslator/gemba","reach":{"status":"ok","spdx":"CC-BY-SA-4.0"}}],"summary":{"ran":8},"by_repo_kind":{"listed":{"samples":8,"ran":8,"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":8,"samples":[{"code_sha256_prefix":"9d3adecc4f3a3d3d","entry":"apply_template","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/gemba_mqm_utils.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/gemba_mqm_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"9d3adecc4f3a3d3d"}},{"code_sha256_prefix":"653b5f6108fab332","entry":"esa_fewshot","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/gemba_esa.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/gemba_esa.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"653b5f6108fab332"}},{"code_sha256_prefix":"276c6f1e2dde96b7","entry":"parse_and_check_numerical_answer","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/prompt.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/prompt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"276c6f1e2dde96b7"}},{"code_sha256_prefix":"45a7b3a8165071ea","entry":"parse_broken_json","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/gemba_mqm_utils.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/gemba_mqm_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"45a7b3a8165071ea"}},{"code_sha256_prefix":"41bc3175e4f55f51","entry":"parse_error_class","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/gemba_mqm_utils.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/gemba_mqm_utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"41bc3175e4f55f51"}},{"code_sha256_prefix":"75c7be0f410ecb14","entry":"parse_numerical_answer","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/prompt.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/prompt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"75c7be0f410ecb14"}},{"code_sha256_prefix":"0a843a55dcf05964","entry":"reformat","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/mtme_tools.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/mtme_tools.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"0a843a55dcf05964"}},{"code_sha256_prefix":"f5420e78d8c6a7a9","entry":"validate_number","repo":"microsofttranslator/gemba","repo_kind":"listed","path":"gemba/prompt.py","file_url":"https://github.com/microsofttranslator/gemba/blob/HEAD/gemba/prompt.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"CC-BY-SA-4.0","inline_ok":false,"mcp_get_code":{"code_sha256":"f5420e78d8c6a7a9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}