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Thinking Fair and Slow: On the Efficacy of Structured Prompts for Debiasing Language Models
Existing debiasing techniques are typically training-based or require access to the model's internals and output distributions, so they are inaccessible to end-users looking to adapt LLM outputs for their particular needs.
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks.
Rhetorical Role Labeling of Legal Documents using Transformers and Graph Neural Networks
A legal document is usually long and dense requiring human effort to parse it.
BITS Pilani at HinglishEval: Quality Evaluation for Code-Mixed Hinglish Text Using Transformers
Code-Mixed text data consists of sentences having words or phrases from more than one language.
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