Papers by Ansar Aynetdinov
NoiseBench: Benchmarking the Impact of Real Label Noise on Named Entity Recognition (2024.emnlp-main)
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| Challenge: | Existing approaches to named entity recognition often contain a significant percentage of incorrect labels for entity types and boundary boundaries. |
| Approach: | They propose a noise-robust learning approach that learns from data with partially incorrect labels. |
| Outcome: | The proposed methods are based on simulated noise and are easier to handle than simulated real noise caused by human error or semi-automatic annotation. |
OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs (2024.naacl-demo)
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| Challenge: | Current research seeks to de-bias such models, or suppress potentially biased answers. |
| Approach: | They present a web demo to test the biases of instruction-tuned Large Language Models . they identify 11 different biase based on a corpus of data . |
| Outcome: | The proposed demo shows that biases in instruction-tuning are explicit and transparent . the demo shows how the model was trained and showcases the web application . |
Pre-Training Curriculum for Multi-Token Prediction in Language Models (2025.acl-long)
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| Challenge: | Multi-token prediction (MTP) is a pre-training objective for language models . prior work has shown that smaller language models struggle with the MTP objective . |
| Approach: | They propose a curriculum learning strategy that uses multiple prediction heads to predict the next tokens at each prediction step. |
| Outcome: | The proposed curriculum improves performance and output quality while retaining the benefits of self-speculative decoding. |