118 citations · 1.4k across the 52 of their papers we have counts for
54 papers
TinyAgent: Function Calling at the Edge
Lutfi Eren Erdogan, Nicholas Lee, Siddharth Jha +7
Recent large language models (LLMs) have enabled the development of advanced agentic systems that can integrate various tools and APIs to fulfill user queries through function call…
Efficient and Scalable Estimation of Tool Representations in Vector Space
Suhong Moon, Siddharth Jha, Lutfi Eren Erdogan +4
Recent advancements in function calling and tool use have significantly enhanced the capabilities of large language models (LLMs) by enabling them to interact with external informa…
Characterizing Prompt Compression Methods for Long Context Inference
Siddharth Jha, Lutfi Eren Erdogan, Sehoon Kim +2
Long context inference presents challenges at the system level with increased compute and memory requirements, as well as from an accuracy perspective in being able to reason over…
AI and Memory Wall
Amir Gholami, Zhewei Yao, Sehoon Kim +3
The availability of unprecedented unsupervised training data, along with neural scaling laws, has resulted in an unprecedented surge in model size and compute requirements for serv…
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Nicholas Lee, Thanakul Wattanawong, Sehoon Kim +6
Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still…
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh +4
LLMs are seeing growing use for applications which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to m…