Papers with large language modeling
RepCodec: A Speech Representation Codec for Speech Tokenization (2024.acl-long)
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| Challenge: | Recent advances in large language models have led to discrete speech tokenization, but this discretization can be costly and impedes performance. |
| Approach: | They propose a new speech representation codec for semantic speech tokenization that reconstructs speech representations from speech encoders like HuBERT or data2vec. |
| Outcome: | The proposed method outperforms the widely used k-means clustering approach in speech understanding and generation. |
IAPT: Instance-Aware Prompt Tuning for Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for prompt tuning require many soft tokens to guarantee performance . large language models still require a large amount of GPU memory and computations to fine-tune . |
| Approach: | They propose to use a parameter-efficient soft prompt generator to generate idiosyncratic soft prompts for each input instruction. |
| Outcome: | The proposed method outperforms the baselines with comparable tunable parameters and is more efficient than LoRA under the single-backbone multi-tenant setting. |
Reward-Weighted Sampling: Enhancing Non-Autoregressive Characteristics in Masked Diffusion LLMs (2025.emnlp-main)
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| Challenge: | Masked diffusion models (MDMs) are promising non-autoregressive alternatives for large language modeling. |
| Approach: | They propose a method that leverages an external reward model to provide a principled global signal during the iterative diffusion process. |
| Outcome: | The proposed method improves non-autoregressive generation orders and performance across evaluation metrics. |