Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation

• URL: http://arxiv.org/abs/2406.05053v1
• Date: Fri, 7 Jun 2024 16:22:51 GMT
• Title: Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation
• Authors: Nachiket Kotalwar, Alkis Gotovos, Adish Singla,
• Abstract summary: We benchmark language models for programming feedback generation across several performance criteria, including quality, cost, time, and data privacy. We develop a fine-tuning pipeline based on GPT-4 generated synthetic data. We showcase the efficacy of fine-tuned Llama3-8B and Phi3-3.8B 4-bit quantized models using WebLLM's in-browser inference engine.
• Score: 22.467879240959686
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primarily focused on improving the quality of generated feedback to achieve human tutors' quality. While quality is an important performance criterion, it is not the only criterion to optimize for real-world educational deployments. In this paper, we benchmark language models for programming feedback generation across several performance criteria, including quality, cost, time, and data privacy. The key idea is to leverage recent advances in the new paradigm of in-browser inference that allow running these models directly in the browser, thereby providing direct benefits across cost and data privacy. To boost the feedback quality of small models compatible with in-browser inference engines, we develop a fine-tuning pipeline based on GPT-4 generated synthetic data. We showcase the efficacy of fine-tuned Llama3-8B and Phi3-3.8B 4-bit quantized models using WebLLM's in-browser inference engine on three different Python programming datasets. We will release the full implementation along with a web app and datasets to facilitate further research on in-browser language models.

Related papers

• Leveraging Web-Crawled Data for High-Quality Fine-Tuning [24.19939701706869]
  We argue that web-crawled data can still serve as a valuable source for high-quality supervised fine-tuning without relying on advanced models like GPT-4. We create a paired training dataset automatically by aligning web-crawled data with a smaller set of high-quality data. Our experiments show that training with the model-transformed data yields better results, surpassing training with only high-quality data by an average score of 9.4% in Chinese math problems.
  arXiv  Detail & Related papers  (2024-08-15T08:12:52Z)
• Towards Effective and Efficient Continual Pre-training of Large Language Models [163.34610964970258]
  Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. This paper presents a technical report for continually pre-training Llama-3 (8B) It significantly enhances the Chinese language ability and scientific reasoning ability of the backbone model.
  arXiv  Detail & Related papers  (2024-07-26T13:55:21Z)
• Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement [102.22911097049953]
  SIMA is a framework that enhances visual and language modality alignment through self-improvement. It employs an in-context self-critic mechanism to select response pairs for preference tuning. We demonstrate that SIMA achieves superior modality alignment, outperforming previous approaches.
  arXiv  Detail & Related papers  (2024-05-24T23:09:27Z)
• Large Language Model Evaluation Via Multi AI Agents: Preliminary results [3.8066447473175304]
  We introduce a novel multi-agent AI model that aims to assess and compare the performance of various Large Language Models (LLMs) Our model consists of eight distinct AI agents, each responsible for retrieving code based on a common description from different advanced language models. We integrate the HumanEval benchmark into our verification agent to assess the generated code's performance, providing insights into their respective capabilities and efficiencies.
  arXiv  Detail & Related papers  (2024-04-01T10:06:04Z)
• QuRating: Selecting High-Quality Data for Training Language Models [64.83332850645074]
  We introduce QuRating, a method for selecting pre-training data that can capture human intuitions about data quality. In this paper, we investigate four qualities - writing style, required expertise, facts & trivia, and educational value. We train a Qur model to learn scalar ratings from pairwise judgments, and use it to annotate a 260B training corpus with quality ratings for each of the four criteria.
  arXiv  Detail & Related papers  (2024-02-15T06:36:07Z)
• EvalCrafter: Benchmarking and Evaluating Large Video Generation Models [70.19437817951673]
  We argue that it is hard to judge the large conditional generative models from the simple metrics since these models are often trained on very large datasets with multi-aspect abilities. Our approach involves generating a diverse and comprehensive list of 700 prompts for text-to-video generation. Then, we evaluate the state-of-the-art video generative models on our carefully designed benchmark, in terms of visual qualities, content qualities, motion qualities, and text-video alignment with 17 well-selected objective metrics.
  arXiv  Detail & Related papers  (2023-10-17T17:50:46Z)
• Automating Human Tutor-Style Programming Feedback: Leveraging GPT-4 Tutor Model for Hint Generation and GPT-3.5 Student Model for Hint Validation [25.317788211120362]
  We investigate the role of generative AI models in providing human tutor-style programming hints. Recent works have benchmarked state-of-the-art models for various feedback generation scenarios. We develop a novel technique, GPT4Hints-GPT3.5Val, to push the limits of generative AI models.
  arXiv  Detail & Related papers  (2023-10-05T17:02:59Z)
• ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models [102.63817106363597]
  We build ELEVATER, the first benchmark to compare and evaluate pre-trained language-augmented visual models. It consists of 20 image classification datasets and 35 object detection datasets, each of which is augmented with external knowledge. We will release our toolkit and evaluation platforms for the research community.
  arXiv  Detail & Related papers  (2022-04-19T10:23:42Z)
• Enhancing Dialogue Generation via Multi-Level Contrastive Learning [57.005432249952406]
  We propose a multi-level contrastive learning paradigm to model the fine-grained quality of the responses with respect to the query. A Rank-aware (RC) network is designed to construct the multi-level contrastive optimization objectives. We build a Knowledge Inference (KI) component to capture the keyword knowledge from the reference during training and exploit such information to encourage the generation of informative words.
  arXiv  Detail & Related papers  (2020-09-19T02:41:04Z)

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.