Papers by Ingroj Shrestha
LLM Bias Detection and Mitigation through the Lens of Desired Distributions (2025.emnlp-main)
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| Challenge: | Prior work on bias mitigation has focused on promoting social equality and demographic parity, but less attention has been given to aligning LLM’s outputs to desired distributions. |
| Approach: | They propose a weighted adaptive loss based fine-tuning method that aligns LLM’s gender–profession output distribution with the desired distribution while preserving language modeling capability. |
| Outcome: | The proposed method achieves near-complete mitigation under equality and 30–75% reduction under real-world settings. |
Debiasing by obfuscating with 007-classifiers promotes fairness in multi-community settings (2025.coling-main)
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| Challenge: | a number of studies have focused on the mitigation of biases in text classifiers. |
| Approach: | They propose an obfuscation-based data augmentation debiasing approach to reduce bias . they add to the training data *obfuses* versions of *all* false positive instances . |
| Outcome: | The proposed approach reduces bias for almost all of the tests without sacrificing false positive rates or F1 scores for minority or majority communities. |
Robust Bias Detection in MLMs and its Application to Human Trait Ratings (2025.findings-naacl)
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| Challenge: | Existing methods to assess demographic bias in MLMs ignore random variability of templates and target concepts, and neglect bias quantification. |
| Approach: | They propose a systematic statistical approach to assess bias in MLMs using mixed models to account for random effects, pseudo-perplexity weights for sentences derived from templates and quantify bias using statistical effect sizes. |
| Outcome: | The proposed method matches on bias scores in magnitude and direction with small to medium effect sizes. |