Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education

• URL: http://arxiv.org/abs/2411.01765v1
• Date: Mon, 04 Nov 2024 03:20:00 GMT
• Title: Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education
• Authors: Alexandra Vassar, Jake Renzella, Emily Ross, Andrew Taylor,
• Abstract summary: This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums.
• Score: 44.17741997623522
• License:
• Abstract: This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums, and explores two research questions: the suitability of university course forums in contributing to fine-tuning datasets, and how supervised fine-tuning can improve LLMs' alignment with educational principles such as constructivism. Initial findings suggest benefits in pedagogical alignment of LLMs, with deeper evaluations required.

Related papers

• Developing a Tutoring Dialog Dataset to Optimize LLMs for Educational Use [1.2277343096128712]
  Large language models (LLMs) have shown promise for scalable educational applications. Our study explores the use of smaller, more affordable LLMs for one-on-one tutoring in the context of solving reading comprehension problems.
  arXiv  Detail & Related papers  (2024-10-25T00:40:21Z)
• A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs [74.35290684163718]
  A primary challenge in large language model (LLM) development is their onerous pre-training cost. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by leveraging a small language model (SLM)
  arXiv  Detail & Related papers  (2024-10-24T14:31:52Z)
• Evaluating Language Models for Generating and Judging Programming Feedback [4.743413681603463]
  Large language models (LLMs) have transformed research and practice across a wide range of domains. We evaluate the efficiency of open-source LLMs in generating high-quality feedback for programming assignments.
  arXiv  Detail & Related papers  (2024-07-05T21:44:11Z)
• The Life Cycle of Large Language Models: A Review of Biases in Education [3.8757867335422485]
  Large Language Models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The integration of LLMs in education technology has renewed concerns over algorithmic bias which may exacerbate educational inequities. This review aims to clarify the complex nature of bias in LLM applications and provide practical guidance for their evaluation to promote educational equity.
  arXiv  Detail & Related papers  (2024-06-03T18:00:28Z)
• Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction [62.409807640887834]
  Chinese Grammatical Error Correction (CGEC) aims to correct all potential grammatical errors in the input sentences. LLMs' performance as correctors on CGEC remains unsatisfactory due to its challenging task focus. We rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.
  arXiv  Detail & Related papers  (2024-02-18T01:40:34Z)
• Pedagogical Alignment of Large Language Models [24.427653091950994]
  Large Language Models (LLMs) provide immediate answers rather than guiding students through the problem-solving process. This paper investigates Learning from Human Preferences (LHP) algorithms to achieve this alignment objective.
  arXiv  Detail & Related papers  (2024-02-07T16:15:59Z)
• Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning [79.32236399694077]
  Low-quality data in the training set are usually detrimental to instruction tuning. We propose a novel method, termed "reflection-tuning" This approach utilizes an oracle LLM to recycle the original training data by introspecting and enhancing the quality of instructions and responses in the data.
  arXiv  Detail & Related papers  (2023-10-18T05:13:47Z)
• Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception [19.335003380399527]
  Large language models (LLMs) offer a promising avenue, with increasing research exploring their educational utility. Our work highlights the role that teachers can play in shaping LLM-supported learning environments.
  arXiv  Detail & Related papers  (2023-10-13T01:21:52Z)
• Aligning Large Language Models with Human: A Survey [53.6014921995006]
  Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect information. This survey presents a comprehensive overview of these alignment technologies, including the following aspects.
  arXiv  Detail & Related papers  (2023-07-24T17:44:58Z)
• A Survey on Large Language Models for Recommendation [77.91673633328148]
  Large Language Models (LLMs) have emerged as powerful tools in the field of Natural Language Processing (NLP) This survey presents a taxonomy that categorizes these models into two major paradigms, respectively Discriminative LLM for Recommendation (DLLM4Rec) and Generative LLM for Recommendation (GLLM4Rec)
  arXiv  Detail & Related papers  (2023-05-31T13:51:26Z)

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.