Papers by Benjamin Schneider
NeuroTrialNER: An Annotated Corpus for Neurological Diseases and Therapies in Clinical Trial Registries (2024.emnlp-main)
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Simona Doneva, Tilia Ellendorff, Beate Sick, Jean-Philippe Goldman, Amelia Cannon, Gerold Schneider, Benjamin Ineichen
| Challenge: | Despite substantial investment, developing new treatments for neurological conditions is a challenging and often unsuccessful endeavour. |
| Approach: | They propose a corpus for named entity recognition that is annotated clinical trial summaries from ClinicalTrials.gov. |
| Outcome: | The proposed corpus is annotated for neurological diseases, therapeutic interventions, and control treatments and achieves a close-to-human performance. |
SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding (2026.findings-acl)
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Songcheng Cai, Zhiheng Lyu, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen
| Challenge: | Existing benchmarks for agentic repository-level code understanding overlook long tail topics and rely on memorized knowledge. |
| Approach: | They propose a repository-level agentic code understanding benchmark that uses long-tail repositories with executable environments to enforce topical balance. |
| Outcome: | Empirically, a Qwen3-8B model trained with the proposed benchmark outperforms GPT-4o by 2.3 points. |
Investigating Wit, Creativity, and Detectability of Large Language Models in Domain-Specific Writing Style Adaptation of Reddit’s Showerthoughts (2024.starsem-1)
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| Challenge: | Recent Large Language Models (LLMs) have shown the ability to generate content that is difficult or impossible to distinguish from human writing. |
| Approach: | They compare GPT-2 and GPT-Neo fine-tuned on Reddit data and GTP-3.5 invoked in a zero-shot manner, against human-authored texts. |
| Outcome: | The proposed model can generate short, creative texts that are difficult to distinguish from human writing, but human evaluators rate them worse than the model. |