To evaluate Large Language Models (LLMs) for question answering (QA), traditional methods typically focus on assessing single-turn responses to given questions. However, this approach doesn't capture the dynamic nature of human-AI interactions, where humans actively seek information through conversation.

Moreover, we analyze the relevance of existing metrics to dynamics metrics, improving them from the perspective of dynamics.

Elliptical pays attention to more relevant information.

The advent of large language models (LLMs) [Brown et al., 2020] has revolutionized the field of

We start by exploring strategies for eliciting explicit, coherent numerical predictive distributions from LLMs.

This paper introduces xRAG, a novel context compression method designed specifically for retrieval-augmented generation.

We argue that the most critical component of a scene synthesis system is to accurately establish the size and position of various objects within a restricted area.

We propose a novel preference modeling approach that utilizes AI feedback and only requires a small amount of human data.