{"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/from-lsat-the-progress-and-challenges-of","title":"From LSAT: The Progress and Challenges of Complex Reasoning","arxiv_id":"2108.00648","date":"2021-08-02","proceeding":null,"authors":["Siyuan Wang","Zhongkun Liu","Wanjun Zhong","Ming Zhou","Zhongyu Wei","Zhumin Chen","Nan Duan"],"abstract":"Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark of human intelligence, it involves a degree of explicit reading comprehension, interpretation of logical knowledge and complex rule application. In this paper, we take a step forward in complex reasoning by systematically studying the three challenging and domain-general tasks of the Law School Admission Test (LSAT), including analytical reasoning, logical reasoning and reading comprehension. We propose a hybrid reasoning system to integrate these three tasks and achieve impressive overall performance on the LSAT tests. The experimental results demonstrate that our system endows itself a certain complex reasoning ability, especially the fundamental reading comprehension and challenging logical reasoning capacities. Further analysis also shows the effectiveness of combining the pre-trained models with the task-specific reasoning module, and integrating symbolic knowledge into discrete interpretable reasoning steps in complex reasoning. We further shed a light on the potential future directions, like unsupervised symbolic knowledge extraction, model interpretability, few-shot learning and comprehensive benchmark for complex reasoning.","url_abs":"https://arxiv.org/abs/2108.00648v1","url_pdf":"https://arxiv.org/pdf/2108.00648v1.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":"from-lsat-the-progress-and-challenges-of","repo_url":"https://github.com/zhongwanjun/AR-LSAT","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"logical-reasoning","task_name":"Logical Reasoning"},{"task_slug":"reading-comprehension","task_name":"Reading Comprehension"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2108.00648","atlas_url":"https://app.syntology.ai/?focus=2108.00648","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.00648"}},"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/zhongwanjun/AR-LSAT","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":11},"by_repo_kind":{"listed":{"samples":11,"ran":0,"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":"305cce488c60e19d","entry":"choose_question_type","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"ARM/pipeline/answer_question_by_tree.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/ARM/pipeline/answer_question_by_tree.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":"305cce488c60e19d"}},{"code_sha256_prefix":"2771f46777734384","entry":"clean_doc","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"ARM/data_analysis/extract_cp_ner_dp_results.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/ARM/data_analysis/extract_cp_ner_dp_results.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":"2771f46777734384"}},{"code_sha256_prefix":"2ee6204e8f1548f9","entry":"clean_string","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"LSTM/utils_multiple_choice.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/LSTM/utils_multiple_choice.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":"2ee6204e8f1548f9"}},{"code_sha256_prefix":"92ca0309c5197304","entry":"convert_examples_to_features","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"LSTM/utils_multiple_choice.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/LSTM/utils_multiple_choice.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":"92ca0309c5197304"}},{"code_sha256_prefix":"3d29d2111932953c","entry":"extract_program","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"ARM/pipeline/extract_program_argument.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/ARM/pipeline/extract_program_argument.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":"3d29d2111932953c"}},{"code_sha256_prefix":"9d60ecb44ac0bbd6","entry":"find_nearest_ent","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"ARM/pipeline/answer_question_by_tree.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/ARM/pipeline/answer_question_by_tree.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":"9d60ecb44ac0bbd6"}},{"code_sha256_prefix":"0316409042f52cb7","entry":"find_wspan","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"ARM/pipeline/extract_program_argument.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/ARM/pipeline/extract_program_argument.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":"0316409042f52cb7"}},{"code_sha256_prefix":"e6cacdf17fcf2a13","entry":"rewrite","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"ARM/pipeline/modify_option.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/ARM/pipeline/modify_option.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":"e6cacdf17fcf2a13"}},{"code_sha256_prefix":"5532594d3fb0fa71","entry":"same","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"ARM/pipeline/extract_program_argument.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/ARM/pipeline/extract_program_argument.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":"5532594d3fb0fa71"}},{"code_sha256_prefix":"0a546b305d274996","entry":"select_field","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"LSTM/main_large.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/LSTM/main_large.py","link_basis":"harvester_set","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":"0a546b305d274996"}},{"code_sha256_prefix":"3c241ecfe3749a6d","entry":"simple_accuracy","repo":"zhongwanjun/AR-LSAT","repo_kind":"listed","path":"LSTM/main_large.py","file_url":"https://github.com/zhongwanjun/AR-LSAT/blob/HEAD/LSTM/main_large.py","link_basis":"harvester_set","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":"3c241ecfe3749a6d"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}