Papers by Chao-Han Yang
Bayesian Example Selection Improves In-Context Learning for Speech, Text and Visual Modalities (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) can adapt to new tasks easily and efficiently in a training-free manner. |
| Approach: | They propose to use eBayesian in-context example selection method to extend the inference probability conditioned on in-constitut examples based on Bayes’ theorem to select in-strategy examples . Experimental results show the efficacy and robustness of their method on various models, tasks and modalities. |
| Outcome: | The proposed method is based on the eBayesian in-context example selection approach. |
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators (2024.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorporated external knowledge. |
| Approach: | They propose a generative paradigm for translation tasks that integrates the diverse translation versions in N-best list. |
| Outcome: | The proposed model outperforms the state-of-the-art model on speech and machine translation benchmarks on various languages. |
FastAdaSP: Multitask-Adapted Efficient Inference for Large Speech Language Model (2024.emnlp-industry)
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| Challenge: | Unlike other modalities, speech has unique temporal dependencies, making efficient inference methods unexplored. |
| Approach: | They propose a weighted token merging framework specifically designed for speech-related tasks to improve the trade-off between efficiency and performance. |
| Outcome: | The proposed method achieves state-of-the-art efficiency-performance trade-off on speech-related tasks. |
Whispering LLaMA: A Cross-Modal Generative Error Correction Framework for Speech Recognition (2023.emnlp-main)
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Srijith Radhakrishnan, Chao-Han Yang, Sumeer Khan, Rohit Kumar, Narsis Kiani, David Gomez-Cabrero, Jesper Tegnér
| Challenge: | Existing methods for generative error correction in automatic speech recognition (ASR) use a two-pass reranking paradigm to generate n-best hypotheses. |
| Approach: | They propose a cross-modal fusion technique for generative error correction in automatic speech recognition. |
| Outcome: | The proposed technique shows a 37.66% improvement in word error rate relative to the n-best Oracle. |
From Descriptive Richness to Bias: Unveiling the Dark Side of Generative Image Caption Enrichment (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have enhanced the capacity of vision-language models to caption visual text. |
| Approach: | They compare standard-format captions and recent GCE processes from the perspectives of gender bias and hallucination. |
| Outcome: | The proposed methods amplify gender bias by 30.9% and increase hallucination by 59.5%. |