7 papers · 1 filter
DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation
Jordan Painter, Dipankar Srirag, Adarsh Kappiyath +3
Large language models increasingly \emph{understand} dialectal English, yet still \emph{produce} only standard, US-leaning English, leaving dialectal generation, the harder half of…
Evaluating Cross-lingual Knowledge Consistency in Code-Mixed vis-a-vis Indian Languages using IndicKLAR
Debajyoti Mazumder, Divyansh Pathak, Prashant Kodali +3
Large language models recall knowledge reliably in English but often fail on the same query posed in a lower-resourced language -- a crosslingual consistency gap that remains under…
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English
Ishmanbir Singh, Dipankar Srirag, Aditya Joshi
Sarcasm is a challenge to sentiment analysis because of the incongruity between stated and implied sentiment. The challenge is exacerbated when the implication may be relevant to a…
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English
Dipankar Srirag, Aditya Joshi, Jordan Painter +1
Despite large language models (LLMs) being known to exhibit bias against non-standard language varieties, there are no known labelled datasets for sentiment analysis of English. To…
Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages
Ali Almutairi, Abdullah Alsuhaibani, Shoaib Jameel +4
Training deep learning networks with minimal supervision has gained significant research attention due to its potential to reduce reliance on extensive labelled data. While self-tr…
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English
Dipankar Srirag, Jordan Painter, Aditya Joshi +1
Existing benchmarks often fail to account for linguistic diversity, like language variants of English. In this paper, we share our experiences from our ongoing project of building…