Language Models are Few-Shot Graders

• URL: http://arxiv.org/abs/2502.13337v1
• Date: Tue, 18 Feb 2025 23:38:21 GMT
• Title: Language Models are Few-Shot Graders
• Authors: Chenyan Zhao, Mariana Silva, Seth Poulsen,
• Abstract summary: We present an ASAG pipeline leveraging state-of-the-art LLMs. We compare the grading performance of three OpenAI models: GPT-4, GPT-4o, and o1-preview. Our findings indicate that providing graded examples enhances grading accuracy, with RAG-based selection outperforming random selection.
• Score: 0.12289361708127876
• License:
• Abstract: Providing evaluations to student work is a critical component of effective student learning, and automating its process can significantly reduce the workload on human graders. Automatic Short Answer Grading (ASAG) systems, enabled by advancements in Large Language Models (LLMs), offer a promising solution for assessing and providing instant feedback for open-ended student responses. In this paper, we present an ASAG pipeline leveraging state-of-the-art LLMs. Our new LLM-based ASAG pipeline achieves better performances than existing custom-built models on the same datasets. We also compare the grading performance of three OpenAI models: GPT-4, GPT-4o, and o1-preview. Our results demonstrate that GPT-4o achieves the best balance between accuracy and cost-effectiveness. On the other hand, o1-preview, despite higher accuracy, exhibits a larger variance in error that makes it less practical for classroom use. We investigate the effects of incorporating instructor-graded examples into prompts using no examples, random selection, and Retrieval-Augmented Generation (RAG)-based selection strategies. Our findings indicate that providing graded examples enhances grading accuracy, with RAG-based selection outperforming random selection. Additionally, integrating grading rubrics improves accuracy by offering a structured standard for evaluation.

Related papers

• A Systematic Examination of Preference Learning through the Lens of Instruction-Following [83.71180850955679]
  We use a novel synthetic data generation pipeline to generate 48,000 instruction unique-following prompts. With our synthetic prompts, we use two preference dataset curation methods - rejection sampling (RS) and Monte Carlo Tree Search (MCTS) Experiments reveal that shared prefixes in preference pairs, as generated by MCTS, provide marginal but consistent improvements. High-contrast preference pairs generally outperform low-contrast pairs; however, combining both often yields the best performance.
  arXiv  Detail & Related papers  (2024-12-18T15:38:39Z)
• Automatic Evaluation for Text-to-image Generation: Task-decomposed Framework, Distilled Training, and Meta-evaluation Benchmark [62.58869921806019]
  We propose a task decomposition evaluation framework based on GPT-4o to automatically construct a new training dataset. We design innovative training strategies to effectively distill GPT-4o's evaluation capabilities into a 7B open-source MLLM, MiniCPM-V-2.6. Experimental results demonstrate that our distilled open-source MLLM significantly outperforms the current state-of-the-art GPT-4o-base baseline.
  arXiv  Detail & Related papers  (2024-11-23T08:06:06Z)
• Language Model Preference Evaluation with Multiple Weak Evaluators [78.53743237977677]
  GED (Preference Graph Ensemble and Denoise) is a novel approach that leverages multiple model-based evaluators to construct preference graphs. In particular, our method consists of two primary stages: aggregating evaluations into a unified graph and applying a denoising process. We provide theoretical guarantees for our framework, demonstrating its efficacy in recovering the ground truth preference structure.
  arXiv  Detail & Related papers  (2024-10-14T01:57:25Z)
• Selective Self-Rehearsal: A Fine-Tuning Approach to Improve Generalization in Large Language Models [19.752712857873043]
  This paper introduces Selective Self-Rehearsal (SSR), a fine-tuning approach that achieves performance comparable to the standard supervised fine-tuning (SFT) By utilizing the model's correct responses, SSR reduces model specialization during the fine-tuning stage. The effectiveness of SSR is demonstrated through experiments on the task of identifying unanswerable queries across various datasets.
  arXiv  Detail & Related papers  (2024-09-07T10:21:03Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis [7.458853474864602]
  Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects and provides deeper market insights to businesses and organizations. Recent studies have proposed using fixed examples for instruction tuning to reformulate ABSA as a generation task. This study proposes an instruction learning method with retrieval-based example ranking for ABSA tasks.
  arXiv  Detail & Related papers  (2024-05-28T10:39:10Z)
• Investigating Automatic Scoring and Feedback using Large Language Models [46.1232919707345]
  This paper explores the efficacy of PEFT-based quantized models, employing classification or regression head, to fine-tune language models for automatic grading and feedback generation. The results show that prediction of grade scores via finetuned LLMs are highly accurate, achieving less than 3% error in grade percentage on average.
  arXiv  Detail & Related papers  (2024-05-01T16:13:54Z)
• Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification [58.720142291102135]
  This case study investigates the task of job classification in a real-world setting. The goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position.
  arXiv  Detail & Related papers  (2023-03-13T14:09:53Z)
• Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning [30.65315081964461]
  We study few-shot learning with pretrained language models (PLMs) from a different perspective. We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples. Our approach FewGen achieves an overall better result across seven classification tasks of the GLUE benchmark than existing few-shot learning methods.
  arXiv  Detail & Related papers  (2022-11-06T06:46:47Z)
• Adaptive Fine-Grained Predicates Learning for Scene Graph Generation [122.4588401267544]
  General Scene Graph Generation (SGG) models tend to predict head predicates and re-balancing strategies prefer tail categories. We propose an Adaptive Fine-Grained Predicates Learning (FGPL-A) which aims at differentiating hard-to-distinguish predicates for SGG. Our proposed model-agnostic strategy significantly boosts performance of benchmark models on VG-SGG and GQA-SGG datasets by up to 175% and 76% on Mean Recall@100, achieving new state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-11T03:37:57Z)

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.