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Subjective Question Generation and Answer Evaluation using NLP

arXiv 2512.17289 ↗First posted 19 Dec 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Natural Language Processing (NLP) is one of the most revolutionary technologies today. It uses artificial intelligence to understand human text and spoken words. It is used for text summarization, grammar checking, sentiment analysis, and advanced chatbots and has many more potential use cases. Furthermore, it has also made its mark on the education sector. Much research and advancements have already been conducted on objective question generation; however, automated subjective question generation and answer evaluation are still in progress. An automated system to generate subjective questions and evaluate the answers can help teachers assess student work and enhance the student's learning experience by allowing them to self-assess their understanding after reading an article or a chapter of a book. This research aims to improve current NLP models or make a novel one for automated subjective question generation and answer evaluation from text input.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 19 Dec 2025.

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References · 10

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  5. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  6. 2023LIMA: Less Is More for Alignmentcited by 1,100
  7. 2023The Falcon Series of Open Language Modelscited by 535
  8. 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
  9. 2023Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Taskscited by 53
  10. 2019Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worldscited by 19

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