Papers by Yeskendir Koishekenov
Memory-efficient NLLB-200: Language-specific Expert Pruning of a Massively Multilingual Machine Translation Model (2023.acl-long)
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| Challenge: | Neural Machine Translation models are based on a Mixture of Experts architecture and can be pruned to remove up to 80% of experts without further finetuning. |
| Approach: | They propose a pruning method that removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
| Outcome: | The proposed pruning method removes up to 80% of experts without further finetuning and with a negligible loss in translation quality. |
Calibrating Verbal Uncertainty as a Linear Feature to Reduce Hallucinations (2025.emnlp-main)
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Ziwei Ji, Lei Yu, Yeskendir Koishekenov, Yejin Bang, Anthony Hartshorn, Alan Schelten, Cheng Zhang, Pascale Fung, Nicola Cancedda
| Challenge: | LLMs often use assertive language when making false claims, resulting in harm and loss of trust. |
| Approach: | They find that a mismatch between semantic and verbal uncertainty is a better predictor of hallucinations than semantic uncertainty alone. |
| Outcome: | a new study shows that mismatch between semantic and verbal uncertainty is better predictor of hallucinations than semantic uncertainty alone. |