{"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/effects-of-diversity-incentives-on-sample","title":"Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation","arxiv_id":"2401.06643","date":"2024-01-12","proceeding":null,"authors":["Jan Cegin","Branislav Pecher","Jakub Simko","Ivan Srba","Maria Bielikova","Peter Brusilovsky"],"abstract":"The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models. However, more research is needed to assess how different prompts, seed data selection strategies, filtering methods, or model settings affect the quality of paraphrased data (and downstream models). In this study, we investigate three text diversity incentive methods well established in crowdsourcing: taboo words, hints by previous outlier solutions, and chaining on previous outlier solutions. Using these incentive methods as part of instructions to LLMs augmenting text datasets, we measure their effects on generated texts lexical diversity and downstream model performance. We compare the effects over 5 different LLMs, 6 datasets and 2 downstream models. We show that diversity is most increased by taboo words, but downstream model performance is highest with hints.","url_abs":"https://arxiv.org/abs/2401.06643v3","url_pdf":"https://arxiv.org/pdf/2401.06643v3.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":"effects-of-diversity-incentives-on-sample","repo_url":"https://github.com/kinit-sk/llm-div-incts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"text-augmentation","task_name":"Text Augmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2401.06643","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.06643"}},"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/kinit-sk/llm-div-incts","reach":{"status":"ok"}}],"summary":{"ran":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"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":3,"samples":[{"code_sha256_prefix":"a20b72e2a38ee414","entry":"change_label_except_for","repo":"kinit-sk/llm-div-incts","repo_kind":"official","path":"datasets/20news/llama2_collect_20news.py","file_url":"https://github.com/kinit-sk/llm-div-incts/blob/HEAD/datasets/20news/llama2_collect_20news.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":"a20b72e2a38ee414"}},{"code_sha256_prefix":"59ba28960d2ca93b","entry":"eval_loop","repo":"kinit-sk/llm-div-incts","repo_kind":"official","path":"datasets/20news/train_bert_news.py","file_url":"https://github.com/kinit-sk/llm-div-incts/blob/HEAD/datasets/20news/train_bert_news.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":"59ba28960d2ca93b"}},{"code_sha256_prefix":"0cc9c3fcc0ff45e8","entry":"get_formatted_structure_cart","repo":"kinit-sk/llm-div-incts","repo_kind":"official","path":"datasets/20news/train_bert_news.py","file_url":"https://github.com/kinit-sk/llm-div-incts/blob/HEAD/datasets/20news/train_bert_news.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":"0cc9c3fcc0ff45e8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}