3

Report generation (RG) is a RAG task that aims to produce a long-form, citation-attributed response to a complex user query. We present the first public, automated, LLM-based implementation of the ARGUE evaluation framework for RG.

For LLMs to be deployed reliably and responsibly, it is essential that their linguistically expressed confidence faithfully reflect their internal uncertainty. This paper presents the first study to systematically and comprehensively benchmark faithful calibration of LLMs and proposes MetaFaith, the first method to improve faithful calibration of any instruction-following LLM in a task-agnostic manner.

This paper discusses the social implications of reinforcement learning with human feedback (RLHF), identifying key social and ethical issues and discussing social impacts for stakeholders. Seven impact areas are examined, including misinformation, AI value-alignment, bias, AI access, cross-cultural dialogue, industry, and workforce.

Weight friction draws from principles in neuroscience and physics to help mitigate catatstrophic forgetting in neural networks. It operates over multiple task domains and has convergence comparable to SGD.

GFCCs can be powerful speech features for use in speech emotion recognition. This preliminary study suggests they are more suitable versus MFCCs for emotion and intensity classification tasks with neural networks.