2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution
Arunabh Srivastava, Mohammad A., Khojastepour +2
Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a multi-agent plan exe…
Re-ranking the Context for Multimodal Retrieval Augmented Generation
Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and re…
RAG-Check: Evaluating Multimodal Retrieval Augmented Generation Performance
Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1
Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations. However, RAG, particul…
Efficient Semantic Communication Through Transformer-Aided Compression
Matin Mortaheb, Mohammad A. Amir Khojastepour, Sennur Ulukus
Transformers, known for their attention mechanisms, have proven highly effective in focusing on critical elements within complex data. This feature can effectively be used to addre…
Semantic Multi-Resolution Communications
Matin Mortaheb, Mohammad A. Amir Khojastepour, Srimat T. Chakradhar +1
Deep learning based joint source-channel coding (JSCC) has demonstrated significant advancements in data reconstruction compared to separate source-channel coding (SSCC). This supe…