Papers by Kshitish Ghate
REALM: A Dataset of Real-World LLM Use Cases (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on LLM adoption and their social implications lack empirical grounding, weakening their validity. |
| Approach: | They propose to integrate a dataset of over 94,000 LLM use cases collected from Reddit and news articles to provide insights into LLM adoption across different domains. |
| Outcome: | The proposed dataset includes over 94,000 LLM use cases collected from Reddit and news articles. |
Personal Information Parroting in Language Models (2026.findings-eacl)
Copied to clipboard
| Challenge: | Modern language models memorize millions of PI instances, increasing privacy risks. |
| Approach: | They develop a model that parrots 13.6% of PI verbatim on a manually curated set of 483 instances . they recommend that pretraining datasets be aggressively filtered and anonymized to minimize PI parroting. |
| Outcome: | The proposed model outperforms the best regex-based PI detectors on a manually curated set of 483 instances of PI. |
Intrinsic Bias is Predicted by Pretraining Data and Correlates with Downstream Performance in Vision-Language Encoders (2025.naacl-long)
Copied to clipboard
| Challenge: | Recent work has found that vision-language models trained under the Contrastive Language Image Pre-training framework contain intrinsic social biases, but how these biase relates to downstream performance has been unclear. |
| Approach: | They present the largest comprehensive analysis to-date of how upstream pre-training factors and downstream performance of CLIP models relate to their intrinsic biases. |
| Outcome: | The proposed model performance analysis shows that the choice of pre-training dataset is the most significant upstream predictor of bias, whereas architectural variations have minimal impact. |
Biases Propagate in Encoder-based Vision-Language Models: A Systematic Analysis From Intrinsic Measures to Zero-shot Retrieval Outcomes (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing encoder-based vision-language models (VLMs) contain intrinsic biases that manifest in biased outputs. |
| Approach: | They propose a framework to measure intrinsic bias propagation by correlating intrinsic bias with extrinsic bias in zero-shot text-to-image and image-totext retrieval. |
| Outcome: | The proposed framework shows that larger/better-performing models exhibit greater bias propagation, raising concerns given the trend towards increasingly complex AI models. |