-
End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2401.00812
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 27
-
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
Paper • 2310.03714 • Published • 36 -
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Paper • 2312.10003 • Published • 44 -
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
Paper • 2308.08155 • Published • 9 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 230
-
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 27 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169
-
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11
-
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 230 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169
-
End-to-End Goal-Driven Web Navigation
Paper • 1602.02261 • Published -
Learning Language Games through Interaction
Paper • 1606.02447 • Published -
Naturalizing a Programming Language via Interactive Learning
Paper • 1704.06956 • Published -
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Paper • 1802.08802 • Published • 1
-
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines
Paper • 2310.03714 • Published • 36 -
ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent
Paper • 2312.10003 • Published • 44 -
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
Paper • 2308.08155 • Published • 9 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 230
-
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 27 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11
-
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11
-
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 27
-
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
Paper • 2401.00812 • Published • 11 -
DynaSaur: Large Language Agents Beyond Predefined Actions
Paper • 2411.01747 • Published • 37 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 230 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 169