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Found 6 papers, 5 papers with code
AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents
Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks.
ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate
Text evaluation has historically posed significant challenges, often demanding substantial labor and time cost.
Arbitrary Few Parameters are Good Enough for Adapting Large-scale Pre-trained Language Models
From our investigations, we find that the model scaling (1) mitigates the effects of the arbitrary module structure on the performance of tuning methods, and (2) enables the tuning methods to optimize fewer parameters to achieve the full-parameter fine-tuning performance.
Plug-and-Play Document Modules for Pre-trained Models
By inserting document plugins into the backbone PTM for downstream tasks, we can encode a document one time to handle multiple tasks, which is more efficient than conventional encoding-task coupling methods that simultaneously encode documents and input queries using task-specific encoders.
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models
This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper.
On Transferability of Prompt Tuning for Natural Language Processing
To explore whether we can improve PT via prompt transfer, we empirically investigate the transferability of soft prompts across different downstream tasks and PLMs in this work.
