3 accepted papers
Large Language Models are commonly benchmarked on a dataset by evaluating all relevant models on all queries in the test set. This can be wasteful for a practitioner who wants to find the best model to deploy—if a model clearly performs worse than others, there is no need to precisely estimate its p…
Large Language Models (LLMs) have achieved remarkable results on a range of standardized tests originally designed to assess human cognitive and psychological traits, such as intelligence and personality. While these results are often interpreted as strong evidence of human-like characteristics in L…
There is a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences. We consider the setting of fairness in content moderation, in which human feedback is used to determine how two comments — referencing different sen…