Search Results for author:
Found 7 papers, 6 papers with code
Counterfactual Token Generation in Large Language Models
Our model allows any large language model to perform counterfactual token generation at almost no cost in comparison with vanilla token generation, it is embarrassingly simple to implement, and it does not require any fine-tuning nor prompt engineering.
Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets
In this paper, our goal is to control how frequently a decision support system based on prediction sets may cause harm, by design.
Towards Human-AI Complementarity with Predictions Sets
Then, we show that the problem of finding the optimal prediction sets under which the human experts achieve the highest average accuracy is NP-hard.
Prediction-Powered Ranking of Large Language Models
Using pairwise comparisons made by humans in the LMSYS Chatbot Arena platform and pairwise comparisons made by three strong large language models, we empirically demonstrate the effectivity of our framework and show that the rank-sets constructed using only pairwise comparisons by the strong large language models are often inconsistent with (the distribution of) human pairwise preferences.
Designing Decision Support Systems Using Counterfactual Prediction Sets
In this context, it has been recently argued that an alternative type of decision support systems may circumvent this challenge.
Improving Expert Predictions with Conformal Prediction
In this work, we develop an automated decision support system that, by design, does not require experts to understand when to trust the system to improve performance.
Reinforcement Learning Under Algorithmic Triage
Methods to learn under algorithmic triage have predominantly focused on supervised learning settings where each decision, or prediction, is independent of each other.
