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ReasonLab: A Controlled and Auditable Evaluation of Prompting Techniques for Multiple-Choice QA
Inder Preet, Shuxin Lin, Dhaval Patel
Probing the capabilities of Large Language Models (LLMs) and building robust solutions for Multiple-Choice Question Answering (MCQA) remain central challenges in natural language u…
Fine-Tuned Thoughts: Leveraging Chain-of-Thought Reasoning for Industrial Asset Health Monitoring
Shuxin Lin, Dhaval Patel, Christodoulos Constantinides
Small Language Models (SLMs) are becoming increasingly popular in specialized fields, such as industrial applications, due to their efficiency, lower computational requirements, an…
Towards Building General Purpose Embedding Models for Industry 4.0 Agents
Christodoulos Constantinides, Shuxin Lin, Dhaval Patel
In this work we focus on improving language models' understanding for asset maintenance to guide the engineer's decisions and minimize asset downtime. Given a set of tasks expresse…
Chat-of-Thought: Collaborative Multi-Agent System for Generating Domain Specific Information
Christodoulos Constantinides, Shuxin Lin, Nianjun Zhou +1
This paper presents a novel multi-agent system called Chat-of-Thought, designed to facilitate the generation of Failure Modes and Effects Analysis (FMEA) documents for industrial a…
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
Christodoulos Constantinides, Dhaval Patel, Shuxin Lin +3
We introduce FailureSensorIQ, a novel Multi-Choice Question-Answering (MCQA) benchmarking system designed to assess the ability of Large Language Models (LLMs) to reason and unders…