Papers by Saksham Khatwani
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification (2025.emnlp-main)
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| Challenge: | Prior work on calibration and uncertainty quantification focuses on individual models, overlooking the potential of model diversity. |
| Approach: | They propose a method that uses Jensen-Shannon Divergence to identify and aggregate well-calibrated subsets of large language models (LLMs) to improve calibration. |
| Outcome: | The proposed method improves accuracy on binary prediction tasks compared to single-model and naive ensemble baselines. |
LogosKG: Hardware-Optimized Scalable and Interpretable Knowledge Graph Retrieval (2026.acl-long)
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He Cheng, Yifu Wu, Saksham Khatwani, Maya Kruse, Dmitriy Dligach, Timothy A. Miller, Majid Afshar, Yanjun Gao
| Challenge: | Existing systems struggle to balance efficiency, scalability, and interpretability. |
| Approach: | They propose a hardware-aligned framework that enables scalable and interpretable k-hop retrieval on large KGs. |
| Outcome: | The proposed framework scales to billion-edge graphs without loss of retrieval fidelity. |