Search Results for author:
Found 7 papers, 3 papers with code
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
Large language models (LLMs) are proven to benefit a lot from retrieval-augmented generation (RAG) in alleviating hallucinations confronted with knowledge-intensive questions.
Chain of Tools: Large Language Model is an Automatic Multi-tool Learner
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extend their utility, empowering them to solve practical tasks.
GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation
Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance.
The Real, the Better: Aligning Large Language Models with Online Human Behaviors
Large language model alignment is widely used and studied to avoid LLM producing unhelpful and harmful responses.
Learning to Use Tools via Cooperative and Interactive Agents
To mitigate these problems, we propose ConAgents, a Cooperative and interactive Agents framework, which coordinates three specialized agents for tool selection, tool execution, and action calibration separately.
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
To address these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to improve fine-grained and coarse-grained knowledge awareness of LLMs.
LOIS: Looking Out of Instance Semantics for Visual Question Answering
Visual question answering (VQA) has been intensively studied as a multimodal task that requires effort in bridging vision and language to infer answers correctly.
