Advancing Student Writing Through Automated Syntax Feedback

• URL: http://arxiv.org/abs/2501.07740v1
• Date: Mon, 13 Jan 2025 23:10:02 GMT
• Title: Advancing Student Writing Through Automated Syntax Feedback
• Authors: Kamyar Zeinalipour, Mehak Mehak, Fatemeh Parsamotamed, Marco Maggini, Marco Gori,
• Abstract summary: This study underscores the pivotal role of syntax feedback in augmenting the syntactic proficiency of students.<n>We introduce a specialized dataset named Essay-Syntax-Instruct designed to enhance the understanding and application of English syntax.
• Score: 10.137657521054356
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study underscores the pivotal role of syntax feedback in augmenting the syntactic proficiency of students. Recognizing the challenges faced by learners in mastering syntactic nuances, we introduce a specialized dataset named Essay-Syntax-Instruct designed to enhance the understanding and application of English syntax among these students. Leveraging the capabilities of Large Language Models (LLMs) such as GPT3.5-Turbo, Llama-2-7b-chat-hf, Llama-2-13b-chat-hf, and Mistral-7B-Instruct-v0.2, this work embarks on a comprehensive fine-tuning process tailored to the syntax improvement task. Through meticulous evaluation, we demonstrate that the fine-tuned LLMs exhibit a marked improvement in addressing syntax-related challenges, thereby serving as a potent tool for students to identify and rectify their syntactic errors. The findings not only highlight the effectiveness of the proposed dataset in elevating the performance of LLMs for syntax enhancement but also illuminate a promising path for utilizing advanced language models to support language acquisition efforts. This research contributes to the broader field of language learning technology by showcasing the potential of LLMs in facilitating the linguistic development of Students.

Related papers

• Linguistic Blind Spots of Large Language Models [14.755831733659699]
  We study the performance of recent large language models (LLMs) on linguistic annotation tasks. We find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. Our results provide insights to inform future advancements in LLM design and development.
  arXiv  Detail & Related papers  (2025-03-25T01:47:13Z)
• Explicit Learning and the LLM in Machine Translation [20.630120942837564]
  This study explores the capacity of large language models (LLMs) for explicit learning. Using constructed languages generated by means as controlled test environments, we designed experiments to assess an LLM's ability to explicitly learn and apply grammar rules. Supervised fine-tuning on chains of thought significantly enhances LLM performance but struggles to generalize to typologically novel or more complex linguistic features.
  arXiv  Detail & Related papers  (2025-03-12T14:57:08Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• Prompt and circumstance: A word-by-word LLM prompting approach to interlinear glossing for low-resource languages [6.4977738682502295]
  We investigate the effectiveness of a retrieval-based LLM prompting approach to glossing, applied to the seven languages from the SIGMORPHON 2023 shared task. Our system beats the BERT-based shared task baseline for every language in the morpheme-level score category. In a case study on Tsez, we ask the LLM to automatically create and follow linguistic instructions, reducing errors on a confusing grammatical feature.
  arXiv  Detail & Related papers  (2025-02-13T21:23:16Z)
• Can LLMs Help Create Grammar?: Automating Grammar Creation for Endangered Languages with In-Context Learning [0.0]
  This paper explores how Large Language Models (LLMs) can assist in generating grammatical information for low-resource languages with limited amount of data.<n>Our methodology involves organising the existing linguistic data and prompting to efficiently enable to generate formal XLE grammar.<n>This study highlights the potential of LLMs to enhance language documentation efforts, providing a cost-effective solution for generating linguistic data and contributing to the preservation of endangered languages.
  arXiv  Detail & Related papers  (2024-12-14T20:43:12Z)
• Boosting the Capabilities of Compact Models in Low-Data Contexts with Large Language Models and Retrieval-Augmented Generation [2.9921619703037274]
  We propose a retrieval augmented generation (RAG) framework backed by a large language model (LLM) to correct the output of a smaller model for the linguistic task of morphological glossing. We leverage linguistic information to make up for the lack of data and trainable parameters, while allowing for inputs from written descriptive grammars interpreted and distilled through an LLM. We show that a compact, RAG-supported model is highly effective in data-scarce settings, achieving a new state-of-the-art for this task and our target languages.
  arXiv  Detail & Related papers  (2024-10-01T04:20:14Z)
• Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data [84.01401439030265]
  Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) We present a simple yet effective automatic process for creating speech-text pair data. Our model demonstrates general capabilities for speech-related tasks without the need for speech instruction-tuning data.
  arXiv  Detail & Related papers  (2024-09-30T07:01:21Z)
• MetaReflection: Learning Instructions for Language Agents using Past Reflections [11.028256182234017]
  We introduce MetaReflection, a novel offline reinforcement learning technique that enhances the performance of Language Agents. We demonstrate the efficacy of MetaReflection by evaluating across multiple domains, including complex logical reasoning, biomedical semantic similarity, open world question answering, and vulnerability threat detection.
  arXiv  Detail & Related papers  (2024-05-13T10:51:43Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models.<n>It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop.<n>Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning [59.07490387145391]
  Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks. Their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. We introduce a novel instruction tuning dataset, INTERS, encompassing 20 tasks across three fundamental IR categories.
  arXiv  Detail & Related papers  (2024-01-12T12:10:28Z)
• Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning [57.74233319453229]
  Large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to the fundamental sentence representation learning task. We propose MultiCSR, a multi-level contrastive sentence representation learning framework that decomposes the process of prompting LLMs to generate a corpus. Our experiments reveal that MultiCSR enables a less advanced LLM to surpass the performance of ChatGPT, while applying it to ChatGPT achieves better state-of-the-art results.
  arXiv  Detail & Related papers  (2023-10-17T03:21:43Z)
• From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning [63.63840740526497]
  We investigate how instruction tuning adjusts pre-trained models with a focus on intrinsic changes. The impact of instruction tuning is then studied by comparing the explanations derived from the pre-trained and instruction-tuned models. Our findings reveal three significant impacts of instruction tuning.
  arXiv  Detail & Related papers  (2023-09-30T21:16:05Z)
• Investigating the Efficacy of Large Language Models in Reflective Assessment Methods through Chain of Thoughts Prompting [0.2552922646705803]
  Chain of Thought(CoT) prompting method has been proposed as a means to enhance LLMs' proficiency in complex reasoning tasks. The primary aim of this research is to assess how well four language models can grade reflective essays of third-year medical students.
  arXiv  Detail & Related papers  (2023-09-30T06:25:27Z)
• Exploring the Integration of Large Language Models into Automatic Speech Recognition Systems: An Empirical Study [0.0]
  This paper explores the integration of Large Language Models (LLMs) into Automatic Speech Recognition (ASR) systems. Our primary focus is to investigate the potential of using an LLM's in-context learning capabilities to enhance the performance of ASR systems.
  arXiv  Detail & Related papers  (2023-07-13T02:31:55Z)
• ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning [97.10875695679499]
  We propose a novel contrastive learning framework named ERICA in pre-training phase to obtain a deeper understanding of the entities and their relations in text. Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks.
  arXiv  Detail & Related papers  (2020-12-30T03:35:22Z)

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.