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Found 6 papers, 6 papers with code
Prompt Optimization with Human Feedback
We apply our APOHF algorithm to various tasks, including optimizing user instructions, prompt optimization for text-to-image generative models, and response optimization with human feedback (i. e., further refining the response using a variant of our APOHF).
Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars
On the other hand, the impact of the instruction, another essential component in the prompt given to the LLM, is often overlooked in existing exemplar selection methods.
DETAIL: Task DEmonsTration Attribution for Interpretable In-context Learning
In-context learning (ICL) allows transformer-based language models that are pre-trained on general text to quickly learn a specific task with a few "task demonstrations" without updating their parameters, significantly boosting their flexibility and generality.
Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformers
This is mainly due to the limited expressive power of the Gaussian process (GP) which is used by BO as a surrogate to model the objective function.
Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients
To this end, we (a) introduce trajectory-informed gradient surrogates which is able to use the history of function queries during optimization for accurate and query-efficient gradient estimation, and (b) develop the technique of adaptive gradient correction using these gradient surrogates to mitigate the aforementioned disparity.
Fair yet Asymptotically Equal Collaborative Learning
In collaborative learning with streaming data, nodes (e. g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed from their latest streaming data.
