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Large language monkeys: Scaling inference compute with repeated sampling
Scaling the amount of compute used to train language models has dramatically improved
their capabilities. However, when it comes to inference, we often limit the amount of compute …
their capabilities. However, when it comes to inference, we often limit the amount of compute …
From llms to llm-based agents for software engineering: A survey of current, challenges and future
With the rise of large language models (LLMs), researchers are increasingly exploring their
applications in var ious vertical domains, such as software engineering. LLMs have …
applications in var ious vertical domains, such as software engineering. LLMs have …
Multi-agent software development through cross-team collaboration
The latest breakthroughs in Large Language Models (LLMs), eg., ChatDev, have catalyzed
profound transformations, particularly through multi-agent collaboration for software …
profound transformations, particularly through multi-agent collaboration for software …
Inference-aware fine-tuning for best-of-n sampling in large language models
Recent studies have indicated that effectively utilizing inference-time compute is crucial for
attaining better performance from large language models (LLMs). In this work, we propose a …
attaining better performance from large language models (LLMs). In this work, we propose a …
What did I do wrong? quantifying LLMs' sensitivity and consistency to prompt engineering
Large Language Models (LLMs) changed the way we design and interact with software
systems. Their ability to process and extract information from text has drastically improved …
systems. Their ability to process and extract information from text has drastically improved …
G-designer: Architecting multi-agent communication topologies via graph neural networks
Recent advancements in large language model (LLM)-based agents have demonstrated
that collective intelligence can significantly surpass the capabilities of individual agents …
that collective intelligence can significantly surpass the capabilities of individual agents …
Prefclm: Enhancing preference-based reinforcement learning with crowdsourced large language models
Preference-based reinforcement learning (PbRL) is emerging as a promising approach to
teaching robots through human comparative feedback without complex reward engineering …
teaching robots through human comparative feedback without complex reward engineering …
A perspective for adapting generalist ai to specialized medical ai applications and their challenges
The integration of Large Language Models (LLMs) into medical applications has sparked
widespread interest across the healthcare industry, from drug discovery and development to …
widespread interest across the healthcare industry, from drug discovery and development to …
Scaling large-language-model-based multi-agent collaboration
Pioneering advancements in large language model-powered agents have underscored the
design pattern of multi-agent collaboration, demonstrating that collective intelligence can …
design pattern of multi-agent collaboration, demonstrating that collective intelligence can …
Turn every application into an agent: Towards efficient human-agent-computer interaction with api-first llm-based agents
Multimodal large language models (MLLMs) have enabled LLM-based agents to directly
interact with application user interfaces (UIs), enhancing agents' performance in complex …
interact with application user interfaces (UIs), enhancing agents' performance in complex …