# Introducing ORKG ASK: an AI-driven Scholarly Literature Search and Exploration System Taking a Neuro-Symbolic Approach

arXiv: https://arxiv.org/abs/2512.16425 · HTML: https://mlsanity.com/paper/2512.16425/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 18 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Automating Research Synthesis with Domain-Specific Large Language Model Fine-Tuning](https://mlsanity.com/paper/2404.08680/) (2024)
- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [CORE-GPT: Combining Open Access research and large language models for credible, trustworthy question answering](https://mlsanity.com/paper/2307.04683/) (2023)
- [ORKG ASK: a Neuro-symbolic Scholarly Search and Exploration System](https://mlsanity.com/paper/2412.04977/) (2024)

## Abstract (arXiv)

As the volume of published scholarly literature continues to grow, finding relevant literature becomes increasingly difficult. With the rise of generative Artificial Intelligence (AI), and particularly Large Language Models (LLMs), new possibilities emerge to find and explore literature. We introduce ASK (Assistant for Scientific Knowledge), an AI-driven scholarly literature search and exploration system that follows a neuro-symbolic approach. ASK aims to provide active support to researchers in finding relevant scholarly literature by leveraging vector search, LLMs, and knowledge graphs. The system allows users to input research questions in natural language and retrieve relevant articles. ASK automatically extracts key information and generates answers to research questions using a Retrieval-Augmented Generation (RAG) approach. We present an evaluation of ASK, assessing the system's usability and usefulness. Findings indicate that the system is user-friendly and users are generally satisfied while using the system.