mlsanityData to 14 Oct 2026

Leveraging Lora Fine-Tuning and Knowledge Bases for Construction Identification

arXiv 2601.13105 ↗First posted 19 Jan 2026cs.CLcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

This study investigates the automatic identification of the English ditransitive construction by integrating LoRA-based fine-tuning of a large language model with a Retrieval-Augmented Generation (RAG) framework.A binary classification task was conducted on annotated data from the British National Corpus. Results demonstrate that a LoRA-fine-tuned Qwen3-8B model significantly outperformed both a native Qwen3-MAX model and a theory-only RAG system. Detailed error analysis reveals that fine-tuning shifts the model's judgment from a surface-form pattern matching towards a more semantically grounded understanding based.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 33 references that are in the map. First posted on arXiv 19 Jan 2026.

Builds on

13 of 33 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  4. 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
  5. 2020A Primer in BERTology: What we know about how BERT workscited by 815
  6. 2019BLiMP: The Benchmark of Linguistic Minimal Pairs for Englishcited by 564
  7. 2018Targeted Syntactic Evaluation of Language Modelscited by 242
  8. 2016What to do about non-standard (or non-canonical) language in NLPcited by 35
  9. 2022The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlativecited by 35
  10. 2020CxGBERT: BERT meets Construction Grammarcited by 32
  11. 2022Neural reality of argument structure constructionscited by 25
  12. 2024Constructions Are So Difficult That Even Large Language Models Get Them Right for the Wrong Reasonscited by 13
1 more
  1. 2023Construction Grammar and Artificial Intelligencecited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026CAIT: A Syntactic Parsing Toolkit for Child-Adult InTeractionscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Optimizing Retrieval-Augmented Generation (RAG) for Colloquial Cantonese: A LoRA-Based Systematic Review
  2. 2024LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters
  3. 2025LoRA-Augmented Generation (LAG) for Knowledge-Intensive Language Tasks
  4. 2024Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge
  5. 2025Dual LoRA: Enhancing LoRA with Magnitude and Direction Updates
  6. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs
  7. 2025LoRA Is Slower Than You Think
  8. 2024Investigating the performance of Retrieval-Augmented Generation and fine-tuning for the development of AI-driven knowledge-based systems