Papers by Martin Foltin
SubLIME: Subset Selection via Rank Correlation Prediction for Data-Efficient LLM Evaluation (2025.acl-long)
Copied to clipboard
Gayathri Saranathan, Cong Xu, Mahammad Parwez Alam, Tarun Kumar, Martin Foltin, Soon Yee Wong, Suparna Bhattacharya
| Challenge: | Large language models and datasets have made benchmark evaluations computationally prohibitive. |
| Approach: | They propose a framework that reduces evaluation costs by 80% to 99% while preserving ranking fidelity. |
| Outcome: | The proposed evaluation reduces evaluation costs by 80% to 99% while preserving ranking fidelity. |
CEBC: Conformal Evidence-Bounded Control for Low-Hallucination Vision–Language Generation (2026.acl-long)
Copied to clipboard
| Challenge: | Existing mitigation approaches reduce hallucinated object mentions at the cost of degraded generation quality or require expensive retraining and task-specific supervision. |
| Approach: | They propose a lightweight framework for low-hallucination vision–language generation . it uses evidence-bounded minimal editing to revise or suppress unsupported referenced entities . |
| Outcome: | The proposed framework reduces hallucinations while maintaining or improving quality metrics. |