Papers by Mateusz Malinowski
A Simple, Yet Effective Approach to Finding Biases in Code Generation (2023.findings-acl)
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| Challenge: | Recent work shows that large language models can generate code on par with humans . however, data-driven approaches may not be sufficient for acquiring reasoning skills . |
| Approach: | They propose a framework that automatically identifies subtle cues a code generation model might exploit . they propose an automated intervention mechanism reminiscent of adversarial testing . |
| Outcome: | The proposed framework can be used as a data transformation technique during fine-tuning, acting as reversal strategy. |
Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models (LLMs) demonstrate increasing proficiency in complex mathematical and algorithmic tasks, yet their geometric reasoning skills are underexplored. |
| Approach: | They propose a framework that enhances LLMs’ reasoning potential through a multi-agent system conducting internal dialogue. |
| Outcome: | The proposed framework enhances LLMs’ reasoning potential through a multi-agent system conducting internal dialogue. |
Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning. (2021.acl-short)
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| Challenge: | a common language model for word math problems lacks mathematical abilities . a data-driven approach to solving word problems is lacking in many areas . |
| Approach: | They propose to train a language model with mathematical abilities to teach word maths . they propose to use semi-formal steps to explain how math results are derived . |
| Outcome: | The proposed model achieves better outcomes than baseline models and on-par with more tailored models. |