565 citations · 569 across the 10 of their papers we have counts for
10 papers
DonorRank: Donor Language Selection for Low-Resource Cross-Lingual Speech Recognition
Akriti Dhasmana, Aarohi Srivastava, David Chiang
Low-resource automatic speech recognition (ASR) commonly relies on cross-lingual transfer, where models are adapted from higher-resource donor languages. However, selecting donors…
Different Perturbations, Different Mechanisms: Understanding Continued Pre-training for Zero-Shot Dialect Robustness
Aarohi Srivastava, David Chiang
Dialectal variation remains a major challenge for multilingual language models. Perturbation-based continued pre-training (CPT) has emerged as a promising approach to improving rob…
IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation
Anjali Kantharuban, Aarohi Srivastava, Fahim Faisal +5
Existing sentence representations primarily encode what a sentence says, rather than how it is expressed, even though the latter is important for many applications. In contrast, we…
Dialect Matters: Cross-Lingual ASR Transfer for Low-Resource Indic Language Varieties
Akriti Dhasmana, Aarohi Srivastava, David Chiang
We conduct an empirical study of cross-lingual transfer using spontaneous, noisy, and code-mixed speech across a wide range of Indic dialects and language varieties. Our results in…
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
We're Calling an Intervention: Exploring Fundamental Hurdles in Adapting Language Models to Nonstandard Text
Aarohi Srivastava, David Chiang
We present a suite of experiments that allow us to understand the underlying challenges of language model adaptation to nonstandard text. We do so by designing interventions that a…
DIALECTBENCH: A NLP Benchmark for Dialects, Varieties, and Closely-Related Languages
Fahim Faisal, Orevaoghene Ahia, Aarohi Srivastava +4
Language technologies should be judged on their usefulness in real-world use cases. An often overlooked aspect in natural language processing (NLP) research and evaluation is langu…
BERTwich: Extending BERT's Capabilities to Model Dialectal and Noisy Text
Aarohi Srivastava, David Chiang
Real-world NLP applications often deal with nonstandard text (e.g., dialectal, informal, or misspelled text). However, language models like BERT deteriorate in the face of dialect…
Fine-Tuning BERT with Character-Level Noise for Zero-Shot Transfer to Dialects and Closely-Related Languages
Aarohi Srivastava, David Chiang
In this work, we induce character-level noise in various forms when fine-tuning BERT to enable zero-shot cross-lingual transfer to unseen dialects and languages. We fine-tune BERT…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…