Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.19999

A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion

Palvika Bansal·Rittika Jindal·Ruichong Wang
View original source ↗Read source document ↗Export metadata ↓

Abstract

Legal-content reproduction requires matching the source exactly in section hierarchy, verbatim text, citations, and amendment spans; a dropped citation or mislabeled amendment carries into the products lawyers rely on. We report an industrial pipeline converting U.S. state legislation into strict, schema-constrained XML, and hypothesize that its accuracy comes from harness engineering, the deterministic tooling built around a model, more than the model itself. Our controlled ablation crosses three harnesses (zero-shot, a single tool-using agent, and a decomposed multi-agent pipeline) with three Claude models on 153 gold bills from two jurisdictions. The harness narrows the model gap: the weakest model in the full pipeline beats the strongest model alone (0.948 versus 0.806, paired bootstrap p less than 0.001). Because the two agentic harnesses share identical tools, their gap comes from orchestration: decomposition plus the persisted state and per-agent context it brings. Across four scored dimensions, the model spread at zero-shot (0.68 to 0.81) narrows to 0.95 at the full pipeline; the residual sits almost entirely in citation normalization, where the canonical identifier is retrieved from an external authority rather than generated from the weights, so a deterministic lookup tool supplies the exactness that a larger model does not.

Explore related subjects

cs.CLcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Palvika Bansal, Rittika Jindal, Ruichong Wang. 2026-10-14. A Strong Harness Narrows the Model Gap: A Comparative Evaluation for Legislative Document-to-XML Conversion. https://arxiv.org/abs/2610.19999

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Learning From How Humans Correct

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we need to relabel the noisy data in our dataset for our industry application. The experiment result shows that our learn-on-correction method improve the classification accuracy from 91.7% to 92.5% in test dataset. The 91.7% accuracy is trained on the corrected dataset, which improve the baseline from 83.3% to 91.7% in test dataset. The accuracy under human evaluation achieves more than 97%.

cs.CL↗
arXiv2026

LLMR: Knowledge Distillation with a Large Language Model-Induced Reward

Large language models have become increasingly popular and demonstrated remarkable performance in various natural language processing (NLP) tasks. However, these models are typically computationally expensive and difficult to be deployed in resource-constrained environments. In this paper, we propose LLMR, a novel knowledge distillation (KD) method based on a reward function induced from large language models. We conducted experiments on multiple datasets in the dialogue generation and summarization tasks. Empirical results demonstrate that our LLMR approach consistently outperforms traditional KD methods in different tasks and datasets.

cs.CL↗
arXiv2026

C-VARC: A Large-Scale Chinese Value Rule Corpus for Value Alignment of Large Language Models

Aligning LLMs with Chinese values requires culturally grounded resources that connect abstract values to concrete behavioral guidance. We introduce C-VARC, a Chinese Value Rule Corpus containing 257,609 Rules of Thumb (RoTs) organized into three dimensions, 12 core values, and 50 derived values. Among them, 36,000 rules underwent independent human review by two annotators (Cohen's kappa>0.85), with disagreements adjudicated by a third. C-VARC supports reusable rule-driven pipelines for both value evaluation and model alignment. Compared with abstract-value guidance, RoT guidance produces greater semantic and character-level n-gram diversity. Across five non-political sensitive themes, the seven-model average selection rate for C-VARC-consistent options is at least 72.0%, while five Chinese annotators show overall rates of at least 87.5%. Conflicting RoT pairs further enable the construction of 404,505 moral dilemmas, with 7,191 of 10,998 evaluated cases eliciting cross-model disagreement. For alignment, C-VARC-Query consistently outperforms matched Value-only supervision on CMoralEval across both Qwen3-8B and Llama-3.1-8B-Instruct, with average gains of 2.97 and 12.82 percentage points, respectively, and positive paired gains across all three seeds for both backbones. It also improves CValues and C-Eval over Value-only supervision on both backbones. C-VARC thus provides a scalable Chinese normative resource for constructing both value-evaluation and alignment supervision.

cs.CL↗