Papers by Leonidas Gee
Fast Vocabulary Transfer for Language Model Compression (2022.emnlp-industry)
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| Challenge: | Existing methods to reduce model size and size are expensive and inefficient for some applications. |
| Approach: | They propose a method that relies on vocabulary transfer to reduce model size and inference time while compromising on performance. |
| Outcome: | The proposed method reduces model size and inference time while compromising on performance. |
Are Compressed Language Models Less Subgroup Robust? (2023.emnlp-main)
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| Challenge: | Existing methods to reduce model size and latency while retaining overall performance are not known about their impact on subgroup robustness. |
| Approach: | They investigate the effects of model compression on subgroup robustness of BERT language models. |
| Outcome: | The proposed compression methods do not worsen the performance on minority subgroups. |
Multi-word Tokenization for Sequence Compression (2023.emnlp-industry)
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| Challenge: | Large Language Models have proven successful at modelling tasks, but they are expensive and slow to scale. |
| Approach: | They propose a Multi-Word Tokenizer that represents frequent multi-word expressions as single tokens. |
| Outcome: | The proposed tokenizer is more robust across shorter sequence lengths, allowing for major speedups via early sequence truncation. |
Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency (2025.findings-naacl)
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| Challenge: | Existing studies have shown that CLMs can generate accurate solutions with no regard for runtime, but at a substantial cost to correctness (down by up to 30%) |
| Approach: | They propose a framework that incorporates correctness and runtime as learning signals via self-generated preference data. |
| Outcome: | The proposed framework reduces the baseline runtimes by 6% and the average length of the generated solutions is reduced by up to 48% on MBPP and 23% on HumanEval. |