6 citations · 16 across the 15 of their papers we have counts for
5 papers · 1 filter
Do Reasoning LLMs Refuse What They Infer in Long Contexts?
Yu Fu, Haz Sameen Shahgir, Huanli Gong +3
Long-context LLMs can infer objectives that are not stated explicitly. This capability is useful for reasoning over documents, code, retrieved evidence, and tool traces, but it als…
Harnessing the Unseen: The Hidden Influence of Intrinsic Knowledge in Long-Context Language Models
Yu Fu, Haz Sameen Shahgir, Hui Liu +3
Recent advances in long-context language models (LCLMs), designed to handle extremely long contexts, primarily focus on utilizing external contextual information, often leaving the…
ExpertGenQA: Open-ended QA generation in Specialized Domains
Haz Sameen Shahgir, Chansong Lim, Jia Chen +2
Generating high-quality question-answer pairs for specialized technical domains remains challenging, with existing approaches facing a tradeoff between leveraging expert examples a…
Too Late to Train, Too Early To Use? A Study on Necessity and Viability of Low-Resource Bengali LLMs
Tamzeed Mahfuz, Satak Kumar Dey, Ruwad Naswan +3
Each new generation of English-oriented Large Language Models (LLMs) exhibits enhanced cross-lingual transfer capabilities and significantly outperforms older LLMs on low-resource…
BanglaCoNER: Towards Robust Bangla Complex Named Entity Recognition
HAZ Sameen Shahgir, Ramisa Alam, Md. Zarif Ul Alam
Named Entity Recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying named entities in text. But much work hasn't been done…