Papers with JavaScript
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities (N18-5)
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| Challenge: | a JavaScript implementation of a convolutional neural network performs feedforward inference completely in the browser. |
| Approach: | They propose a JavaScript implementation that performs feedforward inference completely in the browser. |
| Outcome: | The proposed model performs feedforward inference completely in the browser without server requests . the proposed model is useful for applications with stringent latency requirements or low connectivity . |
StRuCom: A Novel Dataset of Structured Code Comments in Russian (2025.acl-srw)
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| Challenge: | Existing machine learning models for code comment generation are poorly suited for Russian . existing datasets that contain simple comments and docstrings in English are not suitable for function-level documentation generation. |
| Approach: | They propose a dataset specifically designed for Russian code documentation. |
| Outcome: | The first large-scale dataset specifically designed for Russian code documentation is based on human-written comments from GitHub repositories with synthetically generated ones. |
ALGOGEN: Tool-Generated Verifiable Traces for Reliable Algorithm Visualization (2026.findings-acl)
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| Challenge: | Recent LLM-based systems require simulation of algorithm flow and video rendering constraints. |
| Approach: | They propose a paradigm that decouples algorithm execution from rendering. |
| Outcome: | The proposed paradigm reduces execution success rates, element overlap, and inter-frame inconsistencies. |
The slurk Interaction Server Framework: Better Data for Better Dialog Models (2022.lrec-1)
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| Challenge: | slurk is a lightweight dialog data collection and testing tool for crowdsourcing platforms. |
| Approach: | They present a lightweight dialog server that allows to set up dialog data collections and run experiments. |
| Outcome: | The slurk software allows to set up dialog data collections and run experiments with no limitations on the number of participants. |
Scaling Laws for Code: Every Programming Language Matters (2026.findings-acl)
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Jian Yang, Shuyue Guo, Linzheng Chai, Wei Zhang, Aishan Liu, Chuan Hao, Zhoujun Li, Xin Zhao, Xianglong Liu, Weifeng Lv, Bryan Dai
| Challenge: | Existing studies focus on language-agnostic settings, neglecting the inherently multilingual nature of modern software development. |
| Approach: | They propose a proportion-dependent scaling law that prioritizes high-utility languages . they propose PLs to have varying effects during pre-training that affect model performance . |
| Outcome: | The proposed scaling law is based on 1000+ experiments across multiple languages and models. |
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale (2025.findings-acl)
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Linghao Zhang, Junhao Wang, Shilin He, Chaoyun Zhang, Yu Kang, Bowen Li, Jiaheng Wen, Chengxing Xie, Maoquan Wang, Yufan Huang, Elsie Nallipogu, Qingwei Lin, Yingnong Dang, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing studies highlight that dependency-related issues cause over 40% of observed runtime errors on the generated repository. |
| Approach: | They propose a large-scale benchmark and evaluation framework specifically designed to assess LLMs’ capability on dependency inference. |
| Outcome: | The proposed model achieves only a 48% execution pass rate on Python, indicating room for improvement. |
CRUXEVAL-X: A Benchmark for Multilingual Code Reasoning, Understanding and Execution (2025.acl-long)
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Ruiyang Xu, Jialun Cao, Yaojie Lu, Ming Wen, Hongyu Lin, Xianpei Han, Ben He, Shing-Chi Cheung, Le Sun
| Challenge: | Existing code benchmarks focus on code generation, while those for code reasoning are insufficient. |
| Approach: | They propose a multi-lingual code reasoning benchmark that contains 19 programming languages and at least 600 subjects for each language. |
| Outcome: | The proposed model trains on Python and achieves 34.4% Pass@1 in other languages, revealing the cross-language generalization of LLMs. |
WebMMU: A Benchmark for Multimodal Multilingual Website Understanding and Code Generation (2025.emnlp-main)
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Rabiul Awal, Mahsa Massoud, Aarash Feizi, Zichao Li, Suyuchen Wang, Christopher Pal, Aishwarya Agrawal, David Vazquez, Siva Reddy, Juan A. Rodriguez, Perouz Taslakian, Spandana Gella, Sai Rajeswar
| Challenge: | Existing benchmarks focus on specific aspects of web tasks but lack comprehensive coverage. |
| Approach: | They propose a multilingual benchmark that evaluates three core web tasks: (1) website visual question answering, (2) code editing involving HTML/CSS/JavaScript, and (3) mockup-to-code generation. |
| Outcome: | The proposed model performs well on basic information extraction, but struggles with reasoning and grounding, editing code to preserve functionality, and generating design-to-code that maintains hierarchy and supports multilingual content. |
SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) struggle with complex semantic and structural correctness required for automated code repair. |
| Approach: | They propose a hybrid neural-symbolic framework that unifies code synthesis with compiler-informed symbolic feedback to improve LLM-based vulnerability repair. |
| Outcome: | The proposed framework improves code repair accuracy and efficiency over strong SFT and RFT training strategies on the FixJS and CodeFlaws benchmarks. |
MultiFileTest: A Multi-File-Level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms (2026.findings-acl)
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Yibo Wang, Congying Xia, Wenting Zhao, Jiangshu Du, Chunyu Miao, Zhongfen Deng, Philip S. Yu, Chen Xing
| Challenge: | Existing evaluation benchmarks for LLM unit test generation focus on function-level code rather than on more practical, challenging multi-file codebases. |
| Approach: | They propose a multi-file-level benchmark for unit test generation covering Python, Java, and JavaScript. |
| Outcome: | The proposed benchmarks show that most LLMs exhibit moderate performance on MultiFileTest, highlighting the benchmark’s inherent difficulty. |
ODASim: Ordered, Distinctive and Absolute Semantic Similarity for Code Explanation Evaluation (2026.findings-acl)
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Prince Kumar, Vitobha Munigala, Jaydeep Sen, Ashish Mittal, Vishwajeet Kumar, Srikanth G. Tamilselvam
| Challenge: | Existing methods for code explanations fail to distinguish correct from partially or fully incorrect explanations and their similarity scores are poorly calibrated. |
| Approach: | They propose a model-agnostic graded fine-tuning framework that learns calibrated similarity representations between code and explanations to support fine-grained supervision and evaluation. |
| Outcome: | The proposed framework improves F1 score and ECE scores on two embedding models and reduces expected calibration error. |