machine learning ↔ Large Language Models

Relations (1)

related 0.60 — strongly supporting 6 facts

Large Language Models are a specific class of machine learning models, as evidenced by their classification as brittle machine learning models in [1] and their comparative analysis against traditional machine learning frameworks in [2]. Furthermore, research frequently integrates both fields, such as using machine learning to improve LLM theorem proving in [3] or drawing on machine learning practices to evaluate LLM beliefs in [4].

Facts (6)

Sources

Understanding LLM Understanding skywritingspress.ca Skywritings Press 1 fact

claimKaiyu Yang utilizes machine learning and large language models to prove theorems within formal environments such as Coq and Lean.

A Survey on the Theory and Mechanism of Large Language Models arxiv.org arXiv 1 fact

claimThe current landscape of large language models presents new challenges for defining and formalizing concepts like 'robustness', 'fairness', and 'privacy' compared to traditional machine learning, as noted by Chang et al. (2024), Anwar et al. (2024), Dominguez-Olmedo et al. (2025), and Hardt and Mendler-Dünner (2025).

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer 1 fact

accountThe authors conducted a systematic literature review of NLP, machine learning, and knowledge representation research from the last decade to understand approaches for integrating knowledge graphs (KGs) and large language models (LLMs).

Benchmarking Hallucination Detection Methods in RAG - Cleanlab cleanlab.ai Cleanlab 1 fact

claimLarge Language Models (LLMs) are prone to hallucination because they are fundamentally brittle machine learning models that may fail to generate accurate responses even when the retrieved context contains the correct answer, particularly when reasoning across different facts is required.

https://scholar.google.com/citations?view_op=view_... scholar.google.com Daniel A Herrmann, Benjamin A Levinstein · Springer Netherlands 1 fact

claimDaniel A. Herrmann and Benjamin A. Levinstein established four criteria for measuring belief in large language models, drawing from insights in philosophy and machine learning practices.

Call for Papers: KR meets Machine Learning and Explanation kr.org KR 1 fact

claimThe KR 2026 special track 'KR meets Machine Learning and Explanation' invites research on the intersection of Knowledge Representation and Machine Learning, specifically covering topics such as learning symbolic knowledge (ontologies, knowledge graphs, action theories), KR-driven plan computation, logic-based learning, neural-symbolic learning, statistical relational learning, symbolic reinforcement learning, and the mutual use of KR techniques and LLMs.