# Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysis

arXiv: https://arxiv.org/abs/2512.00214 · HTML: https://mlsanity.com/paper/2512.00214/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 11 of 11)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [FOFO: A Benchmark to Evaluate LLMs' Format-Following Capability](https://mlsanity.com/paper/2402.18667/) (2024)
- [MultiBLiMP 1.0: A Massively Multilingual Benchmark of Linguistic Minimal Pairs](https://mlsanity.com/paper/2504.02768/) (2025)
- [ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering](https://mlsanity.com/paper/2505.23723/) (2025)
- [Large Linguistic Models: Investigating LLMs' metalinguistic abilities](https://mlsanity.com/paper/2305.00948/) (2023)
- [Multilingual Gradient Word-Order Typology from Universal Dependencies](https://mlsanity.com/paper/2402.01513/) (2024)
- [Can LLMs Help Create Grammar?: Automating Grammar Creation for Endangered Languages with In-Context Learning](https://mlsanity.com/paper/2412.10960/) (2024)
- [Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks](https://mlsanity.com/paper/2403.17534/) (2024)
- [From Text to CQL: Bridging Natural Language and Corpus Search Engine](https://mlsanity.com/paper/2402.13740/) (2024)

## Built on by (most cited first, 2 of 2)

- [Agent-Driven Corpus Linguistics: A Framework for Autonomous Linguistic Discovery](https://mlsanity.com/paper/2604.07189/) (2026)
- [Evaluating Metalinguistic Knowledge in Large Language Models across the World's Languages](https://mlsanity.com/paper/2602.02182/) (2026)

## Abstract (arXiv)

Empirical grammar research has become increasingly data-driven, but the systematic analysis of annotated corpora still requires substantial methodological and technical effort. We explore how agentic large language models (LLMs) can streamline this process by reasoning over annotated corpora and producing interpretable, data-grounded answers to linguistic questions. We introduce an agentic framework for corpus-grounded grammatical analysis that integrates concepts such as natural-language task interpretation, code generation, and data-driven reasoning. As a proof of concept, we apply it to Universal Dependencies (UD) corpora, testing it on multilingual grammatical tasks inspired by the World Atlas of Language Structures (WALS). The evaluation spans 13 word-order features and over 170 languages, assessing system performance across three complementary dimensions - dominant-order accuracy, order-coverage completeness, and distributional fidelity - which reflect how well the system generalizes, identifies, and quantifies word-order variations. The results demonstrate the feasibility of combining LLM reasoning with structured linguistic data, offering a first step toward interpretable, scalable automation of corpus-based grammatical inquiry.