Papers with CLEF
AutoBool: Reinforcement-Learned LLM for Effective Automatic Systematic Reviews Boolean Query Generation (2026.eacl-long)
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| Challenge: | Existing approaches to generate Boolean queries for systematic reviews are limited by the lack of ground-truth best Boolesan queries. |
| Approach: | They propose a reinforcement learning framework that trains large language models to generate effective Boolean queries for medical systematic reviews. |
| Outcome: | The proposed framework outperforms zero-shot/few-shot prompting on 65 588 topics . it also matches or exceeds the effectiveness of larger GPT-based models using smaller backbones . |
Combining Counting Processes and Classification Improves a Stopping Rule for Technology Assisted Review (2023.findings-emnlp)
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| Challenge: | Experiments on multiple data sets show that the proposed approach consistently improves performance and outperforms several alternative methods. |
| Approach: | They propose to integrate a text classifier into an existing TAR stopping rule to train it without the need for additional annotations. |
| Outcome: | Experiments on multiple data sets show the proposed approach outperforms other methods and achieves the desired level of recall with a lower cost than the existing method based on counting processes alone. |
CogGen: A Cognitively Inspired Recursive Framework for Deep Research Report Generation (2026.findings-acl)
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| Challenge: | Existing approaches to deep research report generation rely on rigid predefined linear workflows, which cause error accumulation and limit in-depth multimodal fusion and report quality. |
| Approach: | They propose a Cognitively inspired recursive framework for deep research report Generation that simulates cognitive writing and abstract visual representation (AVR) they also propose CLEF, a cognitive load evaluation framework, and a benchmark from our world in data. |
| Outcome: | The proposed framework achieves state-of-the-art among open-source systems, surpassing Gemini Deep Research. |
A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition (2021.acl-long)
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| Challenge: | Existing models for named entity recognition (NER) focus on overlapped or discontinuous entities. |
| Approach: | They propose a span-based named entity recognition model that can recognize both overlapped and discontinuous entities jointly. |
| Outcome: | The proposed model can recognize overlapped and discontinuous entities jointly. |
Document Translation vs. Query Translation for Cross-Lingual Information Retrieval in the Medical Domain (2020.acl-main)
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| Challenge: | Existing studies of document translation and query translation are outdated and do not reflect the current advances in machine translation. |
| Approach: | They compare document translation and query translation approaches to cross-lingual information retrieval . they exploit Statistical Machine Translation and Neural Machine Translation paradigms to translate queries into English and English . |
| Outcome: | The proposed approach outperforms the DT approach in translation quality and retrieval quality. |