Evaluating Dialect Robustness of Language Models via Conversation Understanding

• URL: http://arxiv.org/abs/2405.05688v3
• Date: Wed, 11 Dec 2024 23:21:26 GMT
• Title: Evaluating Dialect Robustness of Language Models via Conversation Understanding
• Authors: Dipankar Srirag, Nihar Ranjan Sahoo, Aditya Joshi,
• Abstract summary: We use English language (US English or Indian English) conversations between humans who play the word-guessing game of 'taboo'<n>We formulate two evaluative tasks: target word prediction (TWP) ($textiti.e.$, predict the masked target word in a conversation) and target word selection (TWS) ($textiti.e.$, select the most likely masked target word in a conversation)<n>We create two subsets: en-MV (where en-US is transformed to include dialectal information) and en-TR (where dialectal information is
• Score: 2.8514881296685113
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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