Papers with fully automated
RedactOR: An LLM-Powered Framework for Automatic Clinical Data De-Identification (2025.acl-industry)
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Praphul Singh, Charlotte Dzialo, Jangwon Kim, Sumana Srivatsa, Irfan Bulu, Sri Gadde, Krishnaram Kenthapadi
| Challenge: | Existing de-identification methods suffer from recall errors, limited generalization, and inefficiencies, limiting their real-world applicability. |
| Approach: | They propose a multi-modal framework for de-identifying electronic health records using a retrieval-based entity relexicalization approach. |
| Outcome: | The proposed framework achieves competitive performance while optimizing token usage to reduce LLM costs. |
Normalizing Non-canonical Turkish Texts Using Machine Translation Approaches (P19-2)
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| Challenge: | a study using non-canonical text normalization shows that it can surpass the current best performing system by a large margin. |
| Approach: | They propose a fully automated, context-aware machine translation approach with fewer stages of processing. |
| Outcome: | The proposed approach surpasses the current best-performing system by a large margin . the proposed method is more data-hungry and more data sensitive than other methods . |
WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics (2026.acl-long)
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| Challenge: | Web applications (web apps) are a key arena for large language models to demonstrate their code generation capabilities and commercial potential. |
| Approach: | a new benchmark for large language models (LLMs) is designed to provide real-world user requirements and generalizable evaluation metrics. |
| Outcome: | a new benchmark for large language models (LLMs) provides a real-world, generalizable, and interpretable evaluation score . the benchmark measures user requirements, expression styles and human-preference-aligned weights . a web application can be used to demonstrate its commercial potential, authors say . |
LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are powerful tools for multi-step tasks, but static data pipelines hinder tool learning and cause noisy labels to persist. |
| Approach: | They propose a fully automated, model-aware data evolution framework that tightly integrates data synthesis and model training. |
| Outcome: | Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale. |