Papers by Aleksandar Shtedritski
A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning (2022.aacl-main)
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| Challenge: | Large-scale, pretrained vision-language models are growing in popularity due to impressive performance on downstream tasks with minimal finetuning. |
| Approach: | They propose to apply ranking metrics to image-text representations to investigate bias measures and debiasing methods to reduce various bias measures. |
| Outcome: | The proposed model reduces bias measures with minimal degradation to image-text representations. |
BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology (2023.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities on a wide range of tasks, such as question answering and the generation of coherent text and code. |
| Approach: | They propose a framework for automatic evaluation of large language models on open-ended planning problems and a dataset of biology protocols with corresponding pseudocode representations. |
| Outcome: | The proposed framework evaluates an LLM on a dataset of biology protocols with corresponding pseudocode representations. |
HelloFresh: LLM Evalutions on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits (2024.findings-acl)
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Tim Franzmeyer, Aleksandar Shtedritski, Samuel Albanie, Philip Torr, Joao F. Henriques, Jakob Foerster
| Challenge: | a better understanding of LLM capabilities on real world tasks is vital for safe development and deployment. |
| Approach: | They propose a new LLM called HelloFresh that uses real-world data to measure performance . they backtest the model and find it yields a temporally consistent ranking . |
| Outcome: | The proposed benchmarks outperform static evaluation data and test data on Wikipedia pages. |