{"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/structflowbench-a-structured-flow-benchmark","title":"StructFlowBench: A Structured Flow Benchmark for Multi-turn Instruction Following","arxiv_id":"2502.14494","date":"2025-02-20","proceeding":null,"authors":["Jinnan Li","Jinzhe Li","Yue Wang","Yi Chang","Yuan Wu"],"abstract":"Multi-turn instruction following capability constitutes a core competency of large language models (LLMs) in real-world applications. Existing evaluation benchmarks predominantly focus on fine-grained constraint satisfaction and domain-specific capability assessment, yet overlook the crucial structural dependency between dialogue turns that distinguishes multi-turn from single-turn interactions. This structural dependency not only reflects user intent but also establishes a second dimension for instruction following evaluation beyond constraint satisfaction. To address this gap, we propose StructFlowBench, a multi-turn instruction following benchmark with structural flow modeling. The benchmark innovatively defines a structural flow framework comprising six fundamental inter-turn relationships, which not only introduces novel structural constraints for model evaluation but also serves as generation parameters for creating customized dialogue flows tailored to specific scenarios. Adopting established LLM-based automatic evaluation methodologies, we conduct systematic evaluations of 13 leading open-source and closed-source LLMs. Experimental results reveal significant deficiencies in current models' comprehension of multi-turn dialogue structures. The code is available at \\url{https://github.com/MLGroupJLU/StructFlowBench}.","url_abs":"https://arxiv.org/abs/2502.14494v1","url_pdf":"https://arxiv.org/pdf/2502.14494v1.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":"structflowbench-a-structured-flow-benchmark","repo_url":"https://github.com/mlgroupjlu/structflowbench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"instruction-following","task_name":"Instruction Following"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2502.14494","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2502.14494"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/mlgroupjlu/structflowbench","reach":null}],"summary":{"ran_draft_wrong":1,"ran_violates":1,"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":"956bfce011a67892","entry":"calculate_tcsr","repo":"mlgroupjlu/structflowbench","repo_kind":"official","path":"evaluation/score.py","file_url":"https://github.com/mlgroupjlu/structflowbench/blob/HEAD/evaluation/score.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"956bfce011a67892"}},{"code_sha256_prefix":"79496dad83f6ef55","entry":"round_floats","repo":"mlgroupjlu/structflowbench","repo_kind":"official","path":"evaluation/score.py","file_url":"https://github.com/mlgroupjlu/structflowbench/blob/HEAD/evaluation/score.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"79496dad83f6ef55"}},{"code_sha256_prefix":"c30101fd6013fd62","entry":"process_evaluation_file","repo":"mlgroupjlu/structflowbench","repo_kind":"official","path":"evaluation/score.py","file_url":"https://github.com/mlgroupjlu/structflowbench/blob/HEAD/evaluation/score.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c30101fd6013fd62"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}