Evaluating Dialect Robustness of Language Models via Conversation Understanding

TL;DR

This paper assesses how well large language models handle different English dialects, revealing biases towards US English and demonstrating that fine-tuning with dialectal data can improve dialect understanding.

Contribution

Introduces a novel evaluation framework for dialect robustness using conversation datasets and extends MD3 to create M-MD3 for dialect-specific testing.

Findings

LLMs perform better on US English than Indian English.

GPT models outperform smaller models, but fine-tuning improves smaller models' dialect understanding.

Fine-tuning with dialectal data enhances LLMs' dialect comprehension.

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.