Papers with deep learning frameworks
LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models (2024.acl-demos)
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| Challenge: | Large language models (LLMs) are used for many computational analyses, but approximate string matching packages are not widely used in social science applications. |
| Approach: | The open-source package LinkTransformer provides an end-to-end software for performing record linkage and other data cleaning tasks with transformer LLMs. |
| Outcome: | The open-source package LinkTransformer outperforms standard methods in a variety of languages and settings. |
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules (D19-3)
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| Challenge: | a recent development of spoken dialogue systems has enabled deep learning to achieve state-of-the-art performance. |
| Approach: | They propose a Python-based domain-independent, open-source toolkit for spoken dialogue systems. |
| Outcome: | The proposed toolkit extends OpenDial's Java-based architecture and provides new functions for neural dialogue state tracking and action planning. |
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs (2025.acl-long)
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| Challenge: | specialized language models do not show simultaneous memory saving and inference speedup at deployment time. |
| Approach: | They develop a layer-wise specialization technique that reduces the depth of LLMs by progressive layer dropping and compares it to other algorithms for inference. |
| Outcome: | The proposed model retains LLMs’ capacity in specific domains and achieves inference speedup irrespective of hardware and deep learning frameworks. |
Torch-Struct: Deep Structured Prediction Library (2020.acl-demos)
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| Challenge: | Structured prediction is a key area of machine learning and is difficult to utilize in deep learning frameworks. |
| Approach: | They propose a library for structured prediction that integrates with vectorized, auto-differentiation based frameworks. |
| Outcome: | The library exploits auto-differentiation to produce readable, fast, and testable code. |
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment (2023.emnlp-main)
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| Challenge: | Inference is estimated to make up 80 to 90% of ML cloud computing demand . |
| Approach: | They propose to identify bottlenecks in deep learning frameworks that are causing the disparity in model latency as hardware speed increases over time. |
| Outcome: | The proposed models show that the framework tax is increasing as the hardware speed increases over time. |
Tab2Text - A framework for deep learning with tabular data (2024.findings-emnlp)
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| Challenge: | Tabular data is a foundational part of social sciences and is used to fit supervised learning models. |
| Approach: | They propose a technique for transforming tabular data to text data to improve deep learning models for tabular datasets. |
| Outcome: | The proposed technique improves performance of deep learning models for tabular data. |
TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators (2025.findings-acl)
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Jianling Li, ShangZhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, WangHaojie WangHaojie, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Triton is a high-level Python-like programming language for building efficient GPU kernels. |
| Approach: | They propose a TritonBench benchmark that provides a comprehensive evaluation of Tritonic operators on widely deployed GPUs. |
| Outcome: | The proposed benchmarks show that current LLMs struggle to generate efficient Triton operators on widely deployed GPUs aligned with industry applications. |