Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course

• URL: http://arxiv.org/abs/2408.04645v1
• Date: Fri, 2 Aug 2024 19:49:19 GMT
• Title: Evaluating the Impact of Advanced LLM Techniques on AI-Lecture Tutors for a Robotics Course
• Authors: Sebastian Kahl, Felix Löffler, Martin Maciol, Fabian Ridder, Marius Schmitz, Jennifer Spanagel, Jens Wienkamp, Christopher Burgahn, Malte Schilling,
• Abstract summary: This study evaluates the performance of Large Language Models (LLMs) as an Artificial Intelligence-based tutor for a university course. In particular, different advanced techniques are utilized, such as prompt engineering, Retrieval-Augmented-Generation (RAG), and fine-tuning. Our findings indicate that RAG combined with prompt engineering significantly enhances model responses and produces better factual answers.
• Score: 0.35132421583441026
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study evaluates the performance of Large Language Models (LLMs) as an Artificial Intelligence-based tutor for a university course. In particular, different advanced techniques are utilized, such as prompt engineering, Retrieval-Augmented-Generation (RAG), and fine-tuning. We assessed the different models and applied techniques using common similarity metrics like BLEU-4, ROUGE, and BERTScore, complemented by a small human evaluation of helpfulness and trustworthiness. Our findings indicate that RAG combined with prompt engineering significantly enhances model responses and produces better factual answers. In the context of education, RAG appears as an ideal technique as it is based on enriching the input of the model with additional information and material which usually is already present for a university course. Fine-tuning, on the other hand, can produce quite small, still strong expert models, but poses the danger of overfitting. Our study further asks how we measure performance of LLMs and how well current measurements represent correctness or relevance? We find high correlation on similarity metrics and a bias of most of these metrics towards shorter responses. Overall, our research points to both the potential and challenges of integrating LLMs in educational settings, suggesting a need for balanced training approaches and advanced evaluation frameworks.

Related papers

• Revisiting Robust RAG: Do We Still Need Complex Robust Training in the Era of Powerful LLMs? [69.38149239733994]
  We investigate whether complex robust training strategies remain necessary as model capacity grows. We find that as models become more powerful, the performance gains brought by complex robust training methods drop off dramatically. Our findings suggest that RAG systems can benefit from simpler architectures and training strategies as models become more powerful.
  arXiv  Detail & Related papers  (2025-02-17T03:34:31Z)
• Training an LLM-as-a-Judge Model: Pipeline, Insights, and Practical Lessons [9.954960702259918]
  This paper introduces Themis, a fine-tuned large language model (LLMs) judge that delivers context-aware evaluations. We provide a comprehensive overview of the development pipeline for Themis, highlighting its scenario-dependent evaluation prompts. We introduce two human-labeled benchmarks for meta-evaluation, demonstrating that Themis can achieve high alignment with human preferences in an economical manner.
  arXiv  Detail & Related papers  (2025-02-05T08:35:55Z)
• Automated Feedback in Math Education: A Comparative Analysis of LLMs for Open-Ended Responses [0.0]
  This study aims to explore the potential of Large Language Models (LLMs) in facilitating automated feedback in math education. We employ Mistral, a version of Llama catered to math, and fine-tune this model for evaluating student responses by leveraging a dataset of student responses and teacher-written feedback for middle-school math problems. We evaluate the model's performance in scoring accuracy and the quality of feedback by utilizing judgments from 2 teachers.
  arXiv  Detail & Related papers  (2024-10-29T16:57:45Z)
• Deciphering Cross-Modal Alignment in Large Vision-Language Models with Modality Integration Rate [118.37653302885607]
  We present the Modality Integration Rate (MIR), an effective, robust, and generalized metric to indicate the multi-modal pre-training quality of Large Vision Language Models (LVLMs) MIR is indicative about training data selection, training strategy schedule, and model architecture design to get better pre-training results.
  arXiv  Detail & Related papers  (2024-10-09T17:59:04Z)
• Benchmarks as Microscopes: A Call for Model Metrology [76.64402390208576]
  Modern language models (LMs) pose a new challenge in capability assessment. To be confident in our metrics, we need a new discipline of model metrology.
  arXiv  Detail & Related papers  (2024-07-22T17:52:12Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making. We present a process-based benchmark MR-Ben that demands a meta-reasoning skill. Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models [71.25225058845324]
  Large Language Models (LLMs) have demonstrated revolutionary abilities in language understanding and generation. Retrieval-Augmented Generation (RAG) can offer reliable and up-to-date external knowledge. RA-LLMs have emerged to harness external and authoritative knowledge bases, rather than relying on the model's internal knowledge.
  arXiv  Detail & Related papers  (2024-05-10T02:48:45Z)
• Human-AI Collaborative Essay Scoring: A Dual-Process Framework with LLMs [13.262711792955377]
  This study explores the effectiveness of Large Language Models (LLMs) for automated essay scoring. We propose an open-source LLM-based AES system, inspired by the dual-process theory. We find that our system not only automates the grading process but also enhances the performance and efficiency of human graders.
  arXiv  Detail & Related papers  (2024-01-12T07:50:10Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review [1.6006550105523192]
  Review explores the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs) Examines both foundational and advanced methodologies of prompt engineering, including techniques such as self-consistency, chain-of-thought, and generated knowledge. Review also reflects the essential role of prompt engineering in advancing AI capabilities, providing a structured framework for future research and application.
  arXiv  Detail & Related papers  (2023-10-23T09:15:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.