Showing cs.CLShow all
3 papers · 1 filter
cs.CL2026
The Bottleneck Is Retrieval, Not Reading: Diagnosing Evidence-Based Generation with RAG for Long Context Question Answering
Debopriyo Banerjee, Owais Aijaz, Ahmer Tabassum +3
Retrieval-augmented generation (RAG) is the dominant paradigm for grounding large language models in external corpora, but its similarity-driven, chunked pipeline has structural bl…
cs.CL2026
Trust Before Fusion: QIMG-7 and Source-Aware Resolution for Polluted Multimodal RAG
Saadeldine Eletter, Owais Aijaz, Preslav Nakov
Multimodal retrieval-augmented generation (RAG) is often evaluated with clean evidence, yet real retrieval can return topically relevant but unreliable content: false text and misl…
cs.CL2026
UrduMMLU: A Massive Multitask Benchmark for Urdu Language Understanding
Ahmer Tabassum, Sarfraz Ahmad, Hasan Iqbal +3
Meaningful multilingual evaluation must test models in the target language and educational context. Urdu, spoken by more than 230 million people, lacks a broad MMLU-style benchmark…