{"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/evaluating-dialect-robustness-of-language","title":"Evaluating Dialect Robustness of Language Models via Conversation Understanding","arxiv_id":"2405.05688","date":"2024-05-09","proceeding":null,"authors":["Dipankar Srirag","Nihar Ranjan Sahoo","Aditya Joshi"],"abstract":"With an evergrowing number of LLMs reporting superlative performance for English, their ability to perform equitably for different dialects of English ($\\textit{i.e.}$, dialect robustness) needs to be ascertained. Specifically, we use English language (US English or Indian English) conversations between humans who play the word-guessing game of 'taboo'. We formulate two evaluative tasks: target word prediction (TWP) ($\\textit{i.e.}$, predict the masked target word in a conversation) and target word selection (TWS) ($\\textit{i.e.}$, select the most likely masked target word in a conversation, from among a set of candidate words). Extending MD3, an existing dialectic dataset of taboo-playing conversations, we introduce M-MD3, a target-word-masked version of MD3 with the en-US and en-IN subsets. We create two subsets: en-MV (where en-US is transformed to include dialectal information) and en-TR (where dialectal information is removed from en-IN). We evaluate one open-source (Llama3) and two closed-source (GPT-4/3.5) LLMs. LLMs perform significantly better for US English than Indian English for both TWP and TWS tasks, for all settings, exhibiting marginalisation against the Indian dialect of English. While GPT-based models perform the best, the comparatively smaller models work more equitably after fine-tuning. Our error analysis shows that the LLMs can understand the dialect better after fine-tuning using dialectal data. Our evaluation methodology exhibits a novel way to examine attributes of language models using pre-existing dialogue datasets.","url_abs":"https://arxiv.org/abs/2405.05688v3","url_pdf":"https://arxiv.org/pdf/2405.05688v3.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":"evaluating-dialect-robustness-of-language","repo_url":"https://github.com/dipankarsrirag/eval-dialect-robust","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2405.05688","atlas_url":"https://app.syntology.ai/?focus=2405.05688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05688"}},"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/dipankarsrirag/eval-dialect-robust","reach":null}],"summary":{"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":2,"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":2,"samples":[{"code_sha256_prefix":"14b9af02a0d9bd88","entry":"fine_tune","repo":"dipankarsrirag/eval-dialect-robust","repo_kind":"official","path":"scripts/gpt/fine_tune.py","file_url":"https://github.com/dipankarsrirag/eval-dialect-robust/blob/HEAD/scripts/gpt/fine_tune.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"14b9af02a0d9bd88"}},{"code_sha256_prefix":"6d749884f32e43c3","entry":"group_transcript","repo":"dipankarsrirag/eval-dialect-robust","repo_kind":"official","path":"scripts/data/clean_md3.py","file_url":"https://github.com/dipankarsrirag/eval-dialect-robust/blob/HEAD/scripts/data/clean_md3.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":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6d749884f32e43c3"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}