Citegeist: Automated Generation of Related Work Analysis on the arXiv Corpus

Large Language Models provide significant new opportunities for the generation of high-quality written works. However, their employment in the research community is inhibited by their tendency to hallucinate invalid sources and lack of direct access to a knowledge base of relevant scientific articles. In this work, we present Citegeist: An application pipeline using dynamic Retrieval Augmented Generation (RAG) on the arXiv Corpus to generate a related work section and other citation-backed outputs. For this purpose, we employ a mixture of embedding-based similarity matching, summarization, and multi-stage filtering. To adapt to the continuous growth of the document base, we also present an optimized way of incorporating new and modified papers. To enable easy utilization in the scientific community, we release both, a website (https://citegeist.org), as well as an implementation harness that works with several different LLM implementations.

Unlocking Transparent Alignment Through Enhanced Inverse Constitutional AI for Principle Extraction

Traditional methods for aligning Large Language Models (LLMs), such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO), rely on implicit principles, limiting interpretability. Constitutional AI (CAI) offers an explicit, rule-based framework for guiding model outputs. Building on this, we refine the Inverse Constitutional AI (ICAI) algorithm, which extracts constitutions from preference datasets. By improving principle generation, clustering, and embedding processes, our approach enhances the accuracy and generalizability of extracted principles across synthetic and real-world datasets. While in-context alignment yields modest improvements, our results highlight the potential of these principles to foster more transparent and adaptable alignment methods, offering a promising direction for future advancements beyond traditional fine-tuning.