Papers by Ece Kamar
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection (2022.acl-long)
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| Challenge: | Toxic language detection systems often falsely flag text that contains minority group mentions as toxic . this over-reliance on spurious correlations also causes systems to struggle with detecting implicitly toxic language. |
| Approach: | They develop a machine-generated dataset of toxic and benign statements about 13 minority groups that generates subtly toxic and harmless text with a massive pretrained language model. |
| Outcome: | The proposed method can detect toxic and benign statements on a large scale . it can also detect hate speech on 94.5% of the toxic examples . |
Tandem Training for Language Models (2026.eacl-long)
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| Challenge: | As language models improve, their actions and reasoning will become difficult or impossible for weaker agents and humans to follow, undermining interpretability and oversight. |
| Approach: | They propose a tandem training paradigm that allows models to adapt their language to weaker partners by intermittently and randomly sampling a frozen weak model instead of the strong model being trained. |
| Outcome: | The proposed model is able to produce solutions that are intelligible to weaker agents and humans while keeping task accuracy high. |
Increasing Diversity While Maintaining Accuracy: Text Data Generation with Large Language Models and Human Interventions (2023.acl-long)
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| Challenge: | Large language models (LLMs) can be used to generate text data for training and evaluating other models. |
| Approach: | They propose to use logit suppression and temperature sampling to diversify text generation but at the cost of data accuracy. |
| Outcome: | The proposed approach can increase diversity but at the cost of data accuracy. |