Papers by Tobias Bocklet
Optimized Speculative Sampling for GPU Hardware Accelerators (2024.emnlp-main)
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| Challenge: | Large foundational speech and language models require more memory and computational resources to generate long sequences. |
| Approach: | They propose to optimize speculative sampling for parallel hardware accelerators by combining multiple GPU threads to reduce profiling time. |
| Outcome: | The proposed approach improves profiling time from 6% to 13% without compromising accuracy. |
Evaluation Pitfalls and Challenges in Multimedia Event Extraction (2026.acl-long)
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| Challenge: | Recent work has focused on textual content, but recent work has explored the integration of additional modalities to support more accurate and comprehensive event understanding. |
| Approach: | They propose to analyze the evaluation pitfalls of multimedia event extraction by combining textual and visual inputs to identify events and their arguments across multiple modalities. |
| Outcome: | The proposed model overestimates performance and performance of the proposed model in a series of controlled experiments under a strict evaluation framework. |
SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models (2025.findings-acl)
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Seanie Lee, Dong Bok Lee, Dominik Wagner, Minki Kang, Haebin Seong, Tobias Bocklet, Juho Lee, Sung Ju Hwang
| Challenge: | Deploying large language models (LLMs) requires robust safety guard models to detect and block harmful user prompts. |
| Approach: | They propose a binary router that selectively applies a larger safety guard model to the data that the router considers hard. |
| Outcome: | The proposed method outperforms baselines on multiple benchmark datasets on hard and hard examples. |