Results 51 to 60 of about 55,072 (308)

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

On Evaluating LLM Alignment by Evaluating LLMs as Judges

CoRR
NeurIPS 2025 Camera ...
Yixin Liu, Pengfei Liu, Arman Cohan
openaire   +2 more sources

Machine Learning for Green Solvents: Assessment, Selection and Substitution

Advanced Science, EarlyView.
Environmental regulations have intensified demand for green solvents, but discovery is limited by Solvent Selection Guides (SSGs) that quantify solvent sustainability. Training a machine learning model on GlaxoSmithKline SSG, a database of sustainability metrics for 10,189 solvents, GreenSolventDB is developed. Integrated with Hansen solubility metrics,
Rohan Datta, Janhavi Nistane, Abhishek Sose, Harikrishna Sahu, Rampi Ramprasad   +4 more
wiley   +1 more source

AI-Powered Curricula: Unpacking the Potential and Progress of Generative Technologies in Education

Ubiquity Proceedings
The aim of this narrative review is to understand how Artificial Intelligence (AI), specifically generative AI (e.g. ChatGPT) is thought to be used to support teachers in content authoring and curriculum production.
Duygu Bektik, Thomas Daniel Ullmann, Chris Edwards, Christothea Herodotou, Denise Whitelock   +4 more
doaj   +1 more source

Large Language Models for Investigating Co-Occurrences in Multi-Domain Indoor Environmental Quality [PDF]

E3S Web of Conferences
. Building occupants are continuously exposed to multiple indoor environmental stimuli, including thermal, visual, acoustic, and indoor air quality (IAQ) related factors. Prior research typically examines these multiple stimuli separately.
Li Shundong, Ma Nan
doaj   +1 more source

CS1-LLM: Integrating LLMs into CS1 Instruction

Proceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1
The recent, widespread availability of Large Language Models (LLMs) like ChatGPT and GitHub Copilot may impact introductory programming courses (CS1) both in terms of what should be taught and how to teach it. Indeed, recent research has shown that LLMs are capable of solving the majority of the assignments and exams we previously used in CS1.
Annapurna Vadaparty, Daniel Zingaro, David H. Smith IV, Mounika Padala, Christine Alvarado, Jamie Gorson Benario, Leo Porter 0001   +6 more
openaire   +2 more sources

A Perspective on Interactive Theorem Provers in Physics

Advanced Science, EarlyView.
Into an interactive theorem provers (ITPs), one can write mathematical definitions, theorems and proofs, and the correctness of those results is automatically checked. This perspective goes over the best usage of ITPs within physics and motivates the open‐source community run project PhysLean, the aim of which is to be a library for digitalized physics
Joseph Tooby‐Smith
wiley   +1 more source

Do LLMs act as repositories of causal knowledge?

Journal of Causal Inference
Large language models (LLMs) offer the potential to automate a large number of tasks that previously have not been possible to automate, including some in science.
Huntington-Klein Nick, Murray Eleanor J.
doaj   +1 more source

Self-Reflection Protects Behavior from Volatile Beliefs Linked to Paranoia

Computational Psychiatry
Processing uncertainty may be pathognomonic (characteristic of a disease) for some psychiatric conditions. Some people expect the world to change, even when it doesn’t.
Praveen Suthaharan, Santiago Castiello, Yuen-Siang Ang, Phil Corlett   +3 more
doaj   +1 more source

Evaluating LLMs at Detecting Errors in LLM Responses

CoRR
With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted on error detection of LLM responses. Collecting error annotations on LLM responses is challenging due to the subjective nature of many NLP tasks, and thus previous research ...
Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou, Jihyun Janice Ahn, Yilun Zhao 0001, Xiaoxin Lu, Nan Zhang, Yusen Zhang 0001, Ranran Haoran Zhang, Sujeeth Reddy Vummanthala, Salika Dave, Shaobo Qin, Arman Cohan, Wenpeng Yin 0001, Rui Zhang 0037   +14 more
openaire   +2 more sources