Papers by Vethavikashini Chithrra Raghuram
Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias (2025.findings-naacl)
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| Challenge: | Existing studies have shown that large language models can cause harmful, human-like biases against various demographics. |
| Approach: | They propose a causal formulation for bias measurement in generative language models based on a list of desiderata for designing robust bias benchmarks and a bias-measuring procedure to investigate occupational gender bias. |
| Outcome: | The proposed framework is generalizable and can be extended to include other datasets. |
PAPILLON: Privacy Preservation from Internet-based and Local Language Model Ensembles (2025.naacl-long)
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| Challenge: | Existing research has studied privacy in LLM training data memorization, but it does not prevent users from disclosing PII at inference time. |
| Approach: | They propose a task for chaining API-based and local LLMs that uses public data to construct a benchmark that contains personally identifiable information (PII) |
| Outcome: | The proposed model maintains high response quality for 85.5% of user queries while restricting privacy leakage to only 7.5%. |
Bringing Pedagogy into Focus: Evaluating Virtual Teaching Assistants’ Question-Answering in Asynchronous Learning Environments (2025.findings-emnlp)
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| Challenge: | Existing assessments rely on surface-level metrics and lack sufficient grounding in educational theory . a new framework is proposed to evaluate VTAs in asynchronous learning environments . |
| Approach: | They propose a pedagogically-oriented evaluation framework tailored to asynchronous forum discussions . they construct classifiers using expert annotations of VTA responses on a diverse set of forum posts . |
| Outcome: | The proposed evaluation framework is rooted in learning sciences and tailored to asynchronous forum discussions. |