{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/table-to-text-generation/papers/ran/1","list_of":"/task/table-to-text-generation","task":"Table-to-Text Generation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,11],"of":11,"counts":{"archive_papers_tagged":68,"with_a_code_link":43,"where_syntology_ran_a_sample":11,"not_listed_spam_title":0,"listed":68,"listed_where_code_ran":11,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":9,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":9,"listed_every_run_a_failure_of_syntologys_instrument":2,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/table-to-text-generation/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/detqus-decomposition-enhanced-transformers","slug":"detqus-decomposition-enhanced-transformers","title":"DETQUS: Decomposition-Enhanced Transformers for QUery-focused Summarization","date":"2025-03-07","arxiv_id":"2503.05935","repositories_listed":0,"syntology":{"n":29,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":0,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/detqus-decomposition-enhanced-transformers#ran","syntology_url":"https://syntology.ai/paper/2503.05935","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2503.05935"}},"official":null}},{"url":"/paper/qtsumm-a-new-benchmark-for-query-focused","slug":"qtsumm-a-new-benchmark-for-query-focused","title":"QTSumm: Query-Focused Summarization over Tabular Data","date":"2023-05-23","arxiv_id":"2305.14303","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/qtsumm-a-new-benchmark-for-query-focused#ran","syntology_url":"https://syntology.ai/paper/2305.14303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14303"}},"official":{"repos":["yale-nlp/qtsumm","yilunzhao/qtsumm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/tabgenie-a-toolkit-for-table-to-text","slug":"tabgenie-a-toolkit-for-table-to-text","title":"TabGenie: A Toolkit for Table-to-Text Generation","date":"2023-02-27","arxiv_id":"2302.14169","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/tabgenie-a-toolkit-for-table-to-text#ran","syntology_url":"https://syntology.ai/paper/2302.14169","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.14169"}},"official":{"repos":["kasnerz/tabgenie"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/loft-enhancing-faithfulness-and-diversity-for","slug":"loft-enhancing-faithfulness-and-diversity-for","title":"LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form Control","date":"2023-02-06","arxiv_id":"2302.02962","repositories_listed":1,"syntology":{"n":13,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":1,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/loft-enhancing-faithfulness-and-diversity-for#ran","syntology_url":"https://syntology.ai/paper/2302.02962","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.02962"}},"official":{"repos":["yale-lily/loft"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/reastap-injecting-table-reasoning-skills","slug":"reastap-injecting-table-reasoning-skills","title":"ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples","date":"2022-10-22","arxiv_id":"2210.12374","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/reastap-injecting-table-reasoning-skills#ran","syntology_url":"https://syntology.ai/paper/2210.12374","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12374"}},"official":{"repos":["yale-lily/reastap"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-the-numerical-reasoning-skills-of","slug":"improving-the-numerical-reasoning-skills-of","title":"Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models","date":"2022-05-13","arxiv_id":"2205.06733","repositories_listed":2,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":14,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":14,"n_pointer_only":1,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 14 with no instrument failure: 0 honoured, 0 violated, 14 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improving-the-numerical-reasoning-skills-of#ran","syntology_url":"https://syntology.ai/paper/2205.06733","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.06733"}},"official":{"repos":["ukplab/emnlp2022-reasoning-aware-pretraining","ukplab/starsem2023-arithmetic-based-pretraining"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/robust-controlled-table-to-text-generation-1","slug":"robust-controlled-table-to-text-generation-1","title":"Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning","date":"2022-05-08","arxiv_id":"2205.03972","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/robust-controlled-table-to-text-generation-1#ran","syntology_url":"https://syntology.ai/paper/2205.03972","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.03972"}},"official":{"repos":["luka-group/lattice"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/what-makes-good-in-context-examples-for-gpt-3","slug":"what-makes-good-in-context-examples-for-gpt-3","title":"What Makes Good In-Context Examples for GPT-$3$?","date":"2021-01-17","arxiv_id":"2101.06804","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/what-makes-good-in-context-examples-for-gpt-3#ran","syntology_url":"https://syntology.ai/paper/2101.06804","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.06804"}},"official":null}},{"url":"/paper/prefix-tuning-optimizing-continuous-prompts","slug":"prefix-tuning-optimizing-continuous-prompts","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","date":"2021-01-01","arxiv_id":"2101.00190","repositories_listed":13,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":2,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/prefix-tuning-optimizing-continuous-prompts#ran","syntology_url":"https://syntology.ai/paper/2101.00190","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.00190"}},"official":{"repos":["XiangLi1999/PrefixTuning"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/handling-divergent-reference-texts-when","slug":"handling-divergent-reference-texts-when","title":"Handling Divergent Reference Texts when Evaluating Table-to-Text Generation","date":"2019-06-03","arxiv_id":"1906.01081","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/handling-divergent-reference-texts-when#ran","syntology_url":"https://syntology.ai/paper/1906.01081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01081"}},"official":null}},{"url":"/paper/describing-a-knowledge-base","slug":"describing-a-knowledge-base","title":"Describing a Knowledge Base","date":"2018-09-06","arxiv_id":"1809.01797","repositories_listed":1,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":3,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/describing-a-knowledge-base#ran","syntology_url":"https://syntology.ai/paper/1809.01797","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.01797"}},"official":{"repos":["EagleW/Describing_a_Knowledge_Base"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}}],"record_sha256":"bf2f63514c15253c4aa958f6a569f8b006a199fe09fe529736671ad74dcce653","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}