Papers by Ahmed Alajrami
How does the pre-training objective affect what large language models learn about linguistic properties? (2022.acl-short)
Copied to clipboard
| Challenge: | Several pre-training objectives have been proposed to pre-train language models . but, to our knowledge, no studies have investigated how different pre- training objectives affect what BERT learns about linguistic properties. |
| Approach: | They propose to use masked language modeling to pre-train language models . they propose to optimize a mangled language modeling objective to learn linguistic information . |
| Outcome: | The proposed objectives improve BERT's learning of linguistic properties compared to non-linguistically motivated objectives. |
Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance? (2023.emnlp-main)
Copied to clipboard
| Challenge: | Pre-trained language models capture semantic and syntactic information, but no study has examined how information loss in input token characters affects their performance. |
| Approach: | They address this gap by pre-training language models using small subsets of token characters. |
| Outcome: | The proposed model retains 90% and 77% of the full-token model in standard NLU benchmarks and probing tasks even under extreme settings. |