12 citations · 29 across the 23 of their papers we have counts for
6 papers · 2 filters
Towards Compute-Optimal Many-Shot In-Context Learning
Shahriar Golchin, Yanfei Chen, Rujun Han +7
Long-context large language models (LLMs) are able to process inputs containing up to several million tokens. In the scope of in-context learning (ICL), this translates into using…
Magnet: Multi-turn Tool-use Data Synthesis and Distillation via Graph Translation
Fan Yin, Zifeng Wang, I-Hung Hsu +9
Large language models (LLMs) have exhibited the ability to effectively utilize external tools to address user queries. However, their performance may be limited in complex, multi-t…
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
Zhen Tan, Jun Yan, I-Hung Hsu +12
Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limi…
When One LLM Drools, Multi-LLM Collaboration Rules
Shangbin Feng, Wenxuan Ding, Alisa Liu +10
This position paper argues that in many realistic (i.e., complex, contextualized, subjective) scenarios, one LLM is not enough to produce a reliable output. We challenge the status…
Universal Model Routing for Efficient LLM Inference
Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat +9
Model routing is a simple technique for reducing the inference cost of large language models (LLMs), wherein one maintains a pool of candidate LLMs, and learns to route each prompt…
Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems
Shangbin Feng, Zifeng Wang, Palash Goyal +8
We propose Heterogeneous Swarms, an algorithm to design multi-LLM systems by jointly optimizing model roles and weights. We represent multi-LLM systems as directed acyclic graphs (…