779 citations
- Massachusetts Institute of TechnologyUS13 papers
- Harvard UniversityUS10 papers
- Stanford UniversityUS10 papers
- Technical University of MunichDE9 papers
- Beijing Academy of Artificial IntelligenceCN8 papers
- Monash UniversityAU8 papers
- Shanghai Jiao Tong UniversityCN8 papers
- University of Hong KongHK8 papers
- Harvard University PressUS7 papers
- University of Southern CaliforniaUS7 papers
- Hong Kong University of Science and TechnologyHK6 papers
- Imperial College LondonGB6 papers
6 papers · 2 filters
IterCOMP: Reasoning-aware Adaptive Prompt Compression for Multi-hop Question Answering
JungMin Yun, YoungBin Kim
Multi-hop question answering requires complex reasoning across multiple evidence segments, which often overwhelms retrieval-augmented generation systems with lengthy and noisy cont…
Chunking Methods on Retrieval-Augmented Generation - Effectiveness Evaluation Against Computational Cost and Limitations
Mateusz Śmigielski, Michał Rajkowski, Mateusz Zbrocki +5
Retrieval-Augmented Generation (RAG) has demonstrated significant capabilities in enhancing the performance of Large Language Models (LLMs). One of the key tasks in RAG systems is…
PromptRad: Knowledge-Enhanced Multi-Label Prompt-Tuning for Low-Resource Radiology Report Labeling
Ying-Jia Lin, Tzu-Chin Lo, Ping-Chien Li +3
Automatic report labeling facilitates the identification of clinical findings from unstructured text and enables large-scale annotation for medical imaging research. Existing rule-…
Benchmarking Testing in Automated Theorem Proving
Jongyoon Kim, Hojae Han, Seung-won Hwang
Recent advances in large language models (LLMs) have shown promise in formal theorem proving, yet evaluating semantic correctness remains challenging. Existing evaluations rely on…
SCURank: Ranking Multiple Candidate Summaries with Summary Content Units for Enhanced Summarization
Bo-Jyun Wang, Ying-Jia Lin, Hung-Yu Kao
Small language models (SLMs), such as BART, can achieve summarization performance comparable to large language models (LLMs) via distillation. However, existing LLM-based ranking s…
CGRA-DeBERTa Concept Guided Residual Augmentation Transformer for Theologically Islamic Understanding
Tahir Hussain, Saddam Hussain Khan
Accurate QA over classical Islamic texts remains challenging due to domain specific semantics, long context dependencies, and concept sensitive reasoning. Therefore, a new CGRA DeB…