{"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/avoiding-catastrophic-forgetting-in","title":"Elastic weight consolidation for better bias inoculation","arxiv_id":"2004.14366","date":"2020-04-29","proceeding":"EACL 2021 2","authors":["James Thorne","Andreas Vlachos"],"abstract":"The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification. While fine-tuning models on additional data has been used to mitigate them, a common issue is that of catastrophic forgetting of the original training dataset. In this paper, we show that elastic weight consolidation (EWC) allows fine-tuning of models to mitigate biases while being less susceptible to catastrophic forgetting. In our evaluation on fact verification and NLI stress tests, we show that fine-tuning with EWC dominates standard fine-tuning, yielding models with lower levels of forgetting on the original (biased) dataset for equivalent gains in accuracy on the fine-tuning (unbiased) dataset.","url_abs":"https://arxiv.org/abs/2004.14366v2","url_pdf":"https://arxiv.org/pdf/2004.14366v2.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":"avoiding-catastrophic-forgetting-in","repo_url":"https://github.com/j6mes/eacl2021-debias-finetuning","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"fact-verification","task_name":"Fact Verification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"natural-language-inference","task_name":"Natural Language Inference"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-pair-classification","task_name":"Sentence-Pair Classification"}],"methods":[{"method_slug":"ewc","method_name":"EWC"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2004.14366","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.14366"}},"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/j6mes/eacl2021-debias-finetuning","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran_honours":1,"ran":1,"unverified":2},"by_repo_kind":{"official":{"samples":4,"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":"20922c79a0929bbf","entry":"flatten","repo":"j6mes/eacl2021-debias-finetuning","repo_kind":"official","path":"src/debias_finetuning/readers/preprocessing.py","file_url":"https://github.com/j6mes/eacl2021-debias-finetuning/blob/HEAD/src/debias_finetuning/readers/preprocessing.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":2,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"20922c79a0929bbf"}},{"code_sha256_prefix":"32967e6aa4441efb","entry":"variable","repo":"j6mes/eacl2021-debias-finetuning","repo_kind":"official","path":"src/debias_finetuning/losses/util.py","file_url":"https://github.com/j6mes/eacl2021-debias-finetuning/blob/HEAD/src/debias_finetuning/losses/util.py","link_basis":"plan_row","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"32967e6aa4441efb"}},{"code_sha256_prefix":"0030de3ab50bc1be","entry":"get_gold","repo":"j6mes/eacl2021-debias-finetuning","repo_kind":"official","path":"src/debias_finetuning/readers/preprocessing.py","file_url":"https://github.com/j6mes/eacl2021-debias-finetuning/blob/HEAD/src/debias_finetuning/readers/preprocessing.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"0030de3ab50bc1be"}},{"code_sha256_prefix":"c70ee67c36f7ec0b","entry":"get_non_empty_lines","repo":"j6mes/eacl2021-debias-finetuning","repo_kind":"official","path":"src/debias_finetuning/readers/preprocessing.py","file_url":"https://github.com/j6mes/eacl2021-debias-finetuning/blob/HEAD/src/debias_finetuning/readers/preprocessing.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c70ee67c36f7ec0b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}