Papers by Yassir Fathullah
Efficient LLM Comparative Assessment: A Product of Experts Framework for Pairwise Comparisons (2024.emnlp-main)
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| Challenge: | LLM-as-a-judge approaches are effective but cost scales quadratically with number of candidates, which has practical limitations. |
| Approach: | They propose a Product of Expert (PoE) framework for efficient LLM Comparative Assessment where individual comparisons are considered experts that provide information on a pair’s score difference. |
| Outcome: | The proposed framework can generate score predictions that correlate well with human judgements on multiple NLG tasks with as few as 2% of comparisons. |
Efficient Sample-Specific Encoder Perturbations (2024.naacl-short)
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| Challenge: | Existing approaches to training encoder-decoder systems often depend on teacher-forcing with the likelihood criteria, e.g. next token prediction of the reference sequence. |
| Approach: | They propose an inference-efficient way to modify the behaviour of an encoder-decoder system according to a specific attribute of interest by using a small proxy network. |
| Outcome: | The proposed framework improves the COMET performance of Flan-T5 on Machine Translation and the WER of Whisper foundation models on Speech Recognition. |
Who Needs Decoders? Efficient Estimation of Sequence-Level Attributes with Proxies (2024.eacl-long)
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| Challenge: | Autoregressive decoding is expensive for many sequence-to-sequence tasks, but for some downstream tasks, the actual decoding output is not needed, just attributes of the sequence. |
| Approach: | They propose non-autoregressive proxy models that can efficiently predict scalar-valued sequence-level attributes from the encodings, avoiding the expensive decoding stage. |
| Outcome: | The proposed models outperform ensembles in machine translation (MT) and automatic speech recognition (ASR) while being significantly faster. |
Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they often fail to maintain crucial invariances for specific tasks. |
| Approach: | They propose to distill the capabilities of a computationally intensive, debiased, teacher model into a more compact student model. |
| Outcome: | The proposed model outperforms larger, biased teacher models by reducing the invariances of the student model by incorporating encoders. |
Cross-Lingual Transfer Learning for Speech Translation (2025.naacl-short)
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| Challenge: | Increasing interest in building multilingual foundation models for NLP and speech research has led to limited data collection for training ST systems. |
| Approach: | They propose to use Whisper to explore the behavior of multilingual speech foundation models with restricted data. |
| Outcome: | The proposed model can translate to Chinese with a single language, and it can perform transcriptions in other languages. |
AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs (2024.naacl-long)
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Yassir Fathullah, Chunyang Wu, Egor Lakomkin, Ke Li, Junteng Jia, Yuan Shangguan, Jay Mahadeokar, Ozlem Kalinli, Christian Fuegen, Mike Seltzer
| Challenge: | a new model for speech processing and reasoning uses curated data instead of text. |
| Approach: | They extend the instruction-tuned Llama-2 model with end-to-end speech processing and reasoning abilities without using any carefully curated paired data. |
| Outcome: | The proposed model outperforms or outperfects existing models on synthesized and recorded speech QA tests. |
Subsequence Based Deep Active Learning for Named Entity Recognition (2021.acl-long)
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| Challenge: | Active Learning (AL) has been successfully applied to Deep Learning to drastically reduce the amount of data required to achieve high performance. |
| Approach: | They propose to query subsequences within sentences and propagate their labels to other sentences. |
| Outcome: | The proposed approach achieves high performance on OntoNotes 5.0 and CoNLL 2003 with only 13% of training data and 27% of the training data. |