We have 3 of 15 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
SCSP is a training-free framework that leverages semantic compression for informative token selection in long-context embedding and can be integrated into both zero-shot and fine-tuned models in a plug-and-play manner, consistently improving their performance.
This work proposes an inference-time, plug-and-play Self-Contrastive Steering (SCS) method that constructs unconditional general text embeddings and uses them to refine conditional text embeddings, making them more focused on the target condition.
This work proposes a structured suffix modeling method that incorporates the decoding results from the previous step into the suffix token representations at the current step, allowing them to carry evolving denoising information across generation steps.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
