Papers with LaTeX
LaTeXMT: Machine Translation for LaTeX Documents (2025.emnlp-demos)
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| Challenge: | LaTeXMT is a software solution for structure-preserving, source-to-source translation of LaTex documents. |
| Approach: | They propose a software solution for structure-preserving, source-to-source translation of LaTeX documents . authors propose transformer-based language models which can be trained on plain text . |
| Outcome: | The proposed software is available under the LGPL-3.0 open-source licence and a web version is publicly available. |
Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications (2025.findings-emnlp)
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| Challenge: | a recent study shows that large language models can perform precise text editing tasks. |
| Approach: | InstrEditBench is a benchmark dataset that compares 30,000 structured editing tasks . experimental evaluations show FineEdit outperforms state-of-the-art models . |
| Outcome: | The proposed model outperforms state-of-the-art models on single-turn edits and mistral-7B-OpenOrca on direct edits. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
Neural Machine Translation for Mathematical Formulae (2023.acl-long)
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| Challenge: | a recent paper examines the problem of neural machine translation of mathematical formulae between ambiguous presentation languages and unambiguous content languages. |
| Approach: | They perform translation tasks from LaTeX to Mathematica and from La TeX into semantic LaTaX using convolutional sequence-to-sequence networks. |
| Outcome: | The proposed translations achieve 95.1% and 90.7% exact matches between the two languages. |
Aligned Multi-View Scripts for Universal Chart-to-Code Generation (2026.acl-long)
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| Challenge: | Existing methods for chart-to-code generation are largely Python-centric, limiting practical use and overlooking a critical source of supervision. |
| Approach: | They propose a chart-to-code generation tool that converts a graph image into an executable plotting script. |
| Outcome: | The proposed method outperforms existing systems and is competitive with proprietary systems. |