This dissertation studies whether large language models can perform segment-level social reasoning for digital safety tasks involving coercive and manipulative discourse.
@phdthesis{bihani2025reasoning,title={On the Reasoning Capabilities of Language Models for Detecting Grooming and Coercive Discourse},author={Bihani, Geetanjali},school={Purdue University},year={2025},}
HICSS
The Reliability Paradox: Exploring How Shortcut Learning Undermines Language Model Calibration
This paper investigates whether lower calibration error implies more reliable decision rules for fine-tuned pre-trained language models.
@inproceedings{bihani2025reliability,title={The Reliability Paradox: Exploring How Shortcut Learning Undermines Language Model Calibration},author={Bihani, Geetanjali and Rayz, Julia},booktitle={Proceedings of the 58th Hawaii International Conference on System Sciences},year={2025},}
COLING
Hire Me or Not? Examining Language Model’s Behavior with Occupation Attributes
Damin Zhang, Yi Zhang, Geetanjali Bihani, and 1 more author
This paper investigates language models’ behavior with respect to gender stereotypes in occupation decision-making.
@inproceedings{zhang2025hire,title={Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes},author={Zhang, Damin and Zhang, Yi and Bihani, Geetanjali and Rayz, Julia},booktitle={Proceedings of the 31st International Conference on Computational Linguistics},pages={7891--7911},year={2025},}
AIDPS
Learning Shortcuts: On the Misleading Promise of NLU in Language Models
This chapter surveys shortcut learning in language models and examines how shortcuts can create an illusion of improved natural-language understanding.
@article{bihani2025shortcuts,title={Learning Shortcuts: On the Misleading Promise of NLU in Language Models},author={Bihani, Geetanjali and Rayz, Julia},journal={Artificial Intelligence for Design and Process Science},year={2025},}
2024
NAFIPS
Evaluating Language Models on Grooming Risk Estimation Using Fuzzy Theory
Geetanjali Bihani, Tatiana Ringenberg, and Julia Taylor Rayz
This paper evaluates whether language models can estimate graded risk in a high-stakes digital safety setting using fuzzy theory.
@inproceedings{bihani2024groomingrisk,title={Evaluating Language Models on Grooming Risk Estimation Using Fuzzy Theory},author={Bihani, Geetanjali and Ringenberg, Tatiana and Rayz, Julia Taylor},booktitle={Proceedings of the NAFIPS International Conference on Fuzzy Systems, Soft Computing, and Explainable AI (NAFIPS 2024)},year={2024},}
NAFIPS
A Fuzzy Evaluation of Sentence Encoders on Grooming Risk Classification
A fuzzy-theoretic evaluation of sentence encoders on graded grooming risk classification. Recipient of the Outstanding Student Paper Award.
@inproceedings{bihani2024fuzzysentence,title={A Fuzzy Evaluation of Sentence Encoders on Grooming Risk Classification},author={Bihani, Geetanjali and Rayz, Julia Taylor},booktitle={Proceedings of the NAFIPS International Conference on Fuzzy Systems, Soft Computing, and Explainable AI (NAFIPS 2024)},year={2024},}
Introduces fuzzy binning for more robust estimation of calibration error. Honorable Mention for Best Student Paper.
@inproceedings{bihani2023calibration,title={Calibration Error Estimation Using Fuzzy Binning},author={Bihani, Geetanjali and Rayz, Julia Taylor},booktitle={Fuzzy Information Processing 2023 (NAFIPS 2023), Lecture Notes in Networks and Systems},volume={751},publisher={Springer, Cham},year={2023},}
2022
AAAI
Interpretable Privacy Preservation of Text Representations Using Vector Steganography
Doctoral consortium paper on interpretable privacy preservation of text representations via vector steganography.
@inproceedings{bihani2022stegano,title={Interpretable Privacy Preservation of Text Representations Using Vector Steganography},author={Bihani, Geetanjali},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (Doctoral Consortium)},volume={36},number={11},pages={12872--12873},year={2022},}
Examines information hiding in natural language systems and its interplay with representation and security.
@inproceedings{bihani2022infohiding,title={On Information Hiding in Natural Language Systems},author={Bihani, Geetanjali and Rayz, Julia Taylor},booktitle={The International FLAIRS Conference Proceedings},volume={35},year={2022},}
B&E
MySmartE – An eco-feedback and gaming platform to promote energy conserving thermostat-adjustment behaviors in multi-unit residential buildings
Huijeong Kim, Sangwoo Ham, Marlen Promann, and 10 more authors
A gamified eco-feedback platform that promotes energy-conserving thermostat-adjustment behaviors in multi-unit residential buildings.
@article{kim2022mysmarte,title={MySmartE -- An eco-feedback and gaming platform to promote energy conserving thermostat-adjustment behaviors in multi-unit residential buildings},author={Kim, Huijeong and Ham, Sangwoo and Promann, Marlen and Devarapalli, Hemanth and Bihani, Geetanjali and Ringenberg, Tatiana and Kwarteng, Vanessa and Bilionis, Ilias and Braun, James E. and Rayz, Julia Taylor and Raymond, Leigh and Reimer, Torsten and Karava, Panagiota},journal={Building and Environment},volume={221},pages={109252},year={2022},publisher={Elsevier},}
Applies fuzzy-set classification to utterances that carry multiple simultaneous intents.
@inproceedings{bihani2022multiintent,title={Fuzzy Classification of Multi-intent Utterances},author={Bihani, Geetanjali and Rayz, Julia Taylor},booktitle={Explainable AI and Other Applications of Fuzzy Techniques: Proceedings of NAFIPS 2021},pages={37--51},publisher={Springer International Publishing},year={2022},}
2021
DeeLIO
Low Anisotropy Sense Retrofitting (LASeR): Towards Isotropic and Sense Enriched Representations
This work improves contextual word representations by reducing anisotropy and enriching sense information.
@inproceedings{bihani2021laser,title={Low Anisotropy Sense Retrofitting (LASeR): Towards Isotropic and Sense Enriched Representations},author={Bihani, Geetanjali and Rayz, Julia},booktitle={Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures},pages={81--95},publisher={Association for Computational Linguistics},year={2021},}
2020
WI-IAT
Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings
A semi-supervised analysis of how model choices shape attributive associations in static word embeddings.
@inproceedings{bihani2020modelchoices,title={Model Choices Influence Attributive Word Associations: A Semi-supervised Analysis of Static Word Embeddings},author={Bihani, Geetanjali and Rayz, Julia Taylor},booktitle={Proceedings of the 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology},year={2020},}