Papers by Nearchos Potamitis
Current Advances in LLM Reasoning (2026.acl-tutorials)
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| Challenge: | This tutorial examines comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) advanced inference time methods and post-training methods that aim to make LLMs think more like humans are discussed in this tutorial. |
| Approach: | This tutorial explores comprehensive evaluation strategies to assess the reasoning abilities of large language models (LLMs) and discusses two types of methods to improve models’ reasoning: advanced inference time methods, structured and self-improvement inference methods, and post-training methods, such as RLHF, DPO, and GRPO. |
| Outcome: | This tutorial examines evaluation strategies to assess the reasoning abilities of large language models and discusses two types of methods to improve models’ reasoning. |
Cache Saver: A Modular Framework for Efficient, Affordable, and Reproducible LLM Inference (2025.findings-emnlp)
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Nearchos Potamitis, Lars Henning Klein, Bardia Mohammadi, Chongyang Xu, Attreyee Mukherjee, Niket Tandon, Laurent Bindschaedler, Akhil Arora
| Challenge: | Inference is the major cost throughout the lifecycle of a large language model (LLM). |
| Approach: | They propose a plug-and-play, asynchronous framework that facilitates high-level inference optimizations. |
| Outcome: | The proposed framework reduces cost and CO2 by 35% across methods, tasks, and LLMs. |