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Found 8 papers, 4 papers with code
DogeRM: Equipping Reward Models with Domain Knowledge through Model Merging
Reinforcement learning from human feedback (RLHF) is a popular strategy for aligning large language models (LLMs) with desired behaviors.
Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling
Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages.
Examining Forgetting in Continual Pre-training of Aligned Large Language Models
Recent advances in Large Language Models (LLMs) have exhibited remarkable proficiency across various tasks.
CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation
Conversational search provides a natural interface for information retrieval (IR).
Improving Cascaded Unsupervised Speech Translation with Denoising Back-translation
We use fully unpaired data to train our unsupervised systems and evaluate our results on CoVoST 2 and CVSS.
Model Extraction Attack against Self-supervised Speech Models
In the first stage, SSL is conducted on the large-scale unlabeled corpus to pre-train a small speech model.
On the Utility of Self-supervised Models for Prosody-related Tasks
We find that 13 of the 15 SSL models outperformed the baseline on all the prosody-related tasks.
The Efficacy of Self-Supervised Speech Models for Audio Representations
Extensive experiments on speech and non-speech audio datasets are conducted to investigate the representation abilities of our ensemble method and its single constituent model.
