Papers by Edgar Dobriban
Evaluating the Performance of Large Language Models via Debates (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are evolving and impacting various fields . current methods for evaluation are based on fixed, domain-specific questions or rely on human input, making them unscalable. |
| Approach: | They propose a benchmarking framework based on debates between LLMs, judged by another LLM. |
| Outcome: | The proposed framework achieves rankings that align closely with popular rankings based on human input eliminating the need for costly crowdsourcing. |
Uncertainty in Language Models: Assessment through Rank-Calibration (2024.emnlp-main)
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Xinmeng Huang, Shuo Li, Mengxin Yu, Matteo Sesia, Hamed Hassani, Insup Lee, Osbert Bastani, Edgar Dobriban
| Challenge: | Language Models (LMs) have shown promising performance in natural language generation . however, it is crucial to correctly quantify their level of uncertainty in responding to inputs. |
| Approach: | They propose a framework to quantify uncertainty and confidence for Large Language Models . they use a Rank-calibration framework to measure uncertainty and confident responses . |
| Outcome: | The proposed framework assesses uncertainty and confidence measures for LMs. |