{"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/bridging-context-gaps-leveraging-coreference","title":"Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding","arxiv_id":"2410.01671","date":"2024-10-02","proceeding":null,"authors":["Yanming Liu","Xinyue Peng","Jiannan Cao","Shi Bo","Yanxin Shen","Xuhong Zhang","Sheng Cheng","Xun Wang","Jianwei Yin","Tianyu Du"],"abstract":"Large language models (LLMs) have shown remarkable capabilities in natural language processing; however, they still face difficulties when tasked with understanding lengthy contexts and executing effective question answering. These challenges often arise due to the complexity and ambiguity present in longer texts. To enhance the performance of LLMs in such scenarios, we introduce the Long Question Coreference Adaptation (LQCA) method. This innovative framework focuses on coreference resolution tailored to long contexts, allowing the model to identify and manage references effectively. The LQCA method encompasses four key steps: resolving coreferences within sub-documents, computing the distances between mentions, defining a representative mention for coreference, and answering questions through mention replacement. By processing information systematically, the framework provides easier-to-handle partitions for LLMs, promoting better understanding. Experimental evaluations on a range of LLMs and datasets have yielded positive results, with a notable improvements on OpenAI-o1-mini and GPT-4o models, highlighting the effectiveness of leveraging coreference resolution to bridge context gaps in question answering.","url_abs":"https://arxiv.org/abs/2410.01671v1","url_pdf":"https://arxiv.org/pdf/2410.01671v1.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":[],"tasks":[{"task_slug":"coreference-resolution","task_name":"Coreference Resolution"},{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"coreference-resolution-1","task_name":"coreference-resolution"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2410.01671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.01671"}},"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":"deterministic:regex_extraction","url":"https://github.com/OceannTwT/LQCA","reach":{"status":"ok"}}],"summary":{"ran":5,"unverified":5},"by_repo_kind":{"found_in_text":{"samples":10,"ran":5,"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":10,"samples":[{"code_sha256_prefix":"98178cceea3654c1","entry":"chat_completion_request","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"GPTFactory/GPTFactory.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/GPTFactory/GPTFactory.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"98178cceea3654c1"}},{"code_sha256_prefix":"2e4cd3f8b7c0198c","entry":"f1_score","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"predict.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/predict.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2e4cd3f8b7c0198c"}},{"code_sha256_prefix":"12872ca86b6d747e","entry":"load_from_file","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"utils.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"12872ca86b6d747e"}},{"code_sha256_prefix":"80f89e5ac2bbf130","entry":"normalize_answer","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"predict.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/predict.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"80f89e5ac2bbf130"}},{"code_sha256_prefix":"f131f21e7e7b27f7","entry":"update_avg_f1","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"predict.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/predict.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f131f21e7e7b27f7"}},{"code_sha256_prefix":"928e6cabdccb6faa","entry":"ids_to_tokens","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"slide.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/slide.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"928e6cabdccb6faa"}},{"code_sha256_prefix":"f1a772f125f7b119","entry":"print_exp","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"utils.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f1a772f125f7b119"}},{"code_sha256_prefix":"28145bbcf1d4f9ea","entry":"print_now","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"utils.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/utils.py","link_basis":"plan_row","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"28145bbcf1d4f9ea"}},{"code_sha256_prefix":"1853c17733ecfd0d","entry":"slide_sent_token_chunks_with_positions","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"slide.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/slide.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1853c17733ecfd0d"}},{"code_sha256_prefix":"9b58ec220fe99ab5","entry":"slide_sent_token_chunks_with_positions_2","repo":"OceannTwT/LQCA","repo_kind":"found_in_text","path":"slide.py","file_url":"https://github.com/OceannTwT/LQCA/blob/HEAD/slide.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9b58ec220fe99ab5"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}