3 accepted papers
Large language models have demonstrated strong capabilities to learn in-context, where exemplar input-output pairings are appended to the prompt for demonstration. However, existing work has demonstrated the ability of models to learn lexical and label biases in-context, which negatively impacts bot…
In-context learning is an emergent learning paradigm that enables an LLM to learn an unseen task by seeing a number of demonstrations in the context window. The quality of the demonstrations is of paramount importance as 1) context window size limitations restrict the number of demonstrations that c
Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label detection. In particular, due to the desirable axiomatic properties, several Shapley value approximations have been propose…