Language Models Get a Gender Makeover: Mitigating Gender Bias with Few-Shot Data Interventions

Societal biases present in pre-trained large language models are a critical issue as these models have been shown to propagate biases in countless downstream applications, rendering them unfair towards specific groups of people. Since large-scale retraining of these models from scratch is both time and compute-expensive, a variety of approaches have been previously proposed that de-bias a pre-trained model. While the majority of current state-of-the-art debiasing methods focus on changes to the training regime, in this paper, we propose data intervention strategies as a powerful yet simple technique to reduce gender bias in pre-trained models. Specifically, we empirically show that by fine-tuning a pre-trained model on only 10 de-biased (intervened) training examples, the tendency to favor any gender is significantly reduced. Since our proposed method only needs a few training examples, our few-shot debiasing approach is highly feasible and practical. Through extensive experimentation, we show that our debiasing technique performs better than competitive state-of-the-art baselines with minimal loss in language modeling ability.

Fake News Early Detection: A Theory-driven Model

The explosive growth of fake news and its erosion of democracy, justice, and public trust has significantly increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. However, to achieve fake news early detection, one is only provided with limited to no information on news propagation; hence, motivating the need to develop approaches that can detect fake news by focusing mainly on news content. In this paper, a theory-driven model is proposed for fake news detection. The method investigates news content at various levels: lexicon-level, syntax-level, semantic-level and discourse-level. We represent news at each level, relying on well-established theories in social and forensic psychology. Fake news detection is then conducted within a supervised machine learning framework. As an interdisciplinary research, our work explores potential fake news patterns, enhances the interpretability in fake news feature engineering, and studies the relationships among fake news, deception/disinformation, and clickbaits. Experiments conducted on two real-world datasets indicate that the proposed method can outperform the state-of-the-art and enable fake news early detection, even when there is limited content information.

AI in Game Playing: Sokoban Solver

Artificial Intelligence is becoming instrumental in a variety of applications. Games serve as a good breeding ground for trying and testing these algorithms in a sandbox with simpler constraints in comparison to real life. In this project, we aim to develop an AI agent that can solve the classical Japanese game of Sokoban using various algorithms and heuristics and compare their performances through standard metrics.

Automatic 3D Reconstruction for Symmetric Shapes

Generic 3D reconstruction from a single image is a difficult problem. A lot of data loss occurs in the projection. A domain based approach to reconstruction where we solve a smaller set of problems for a particular use case lead to greater returns. The project provides a way to automatically generate full 3-D renditions of actual symmetric images that have some prior information provided in the pipeline by a recognition algorithm. We provide a critical analysis on how this can be enhanced and improved to provide a general reconstruction framework for automatic reconstruction for any symmetric shape.