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Found 8 papers, 8 papers with code
FuseChat: Knowledge Fusion of Chat Models
Recently, \textsc{FuseLLM} introduced the concept of knowledge fusion to transfer the collective knowledge of multiple structurally varied LLMs into a target LLM through lightweight continual training.
Knowledge Verification to Nip Hallucination in the Bud
While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as \emph{hallucination}.
Knowledge Fusion of Large Language Models
In this paper, we introduce the notion of knowledge fusion for LLMs, aimed at combining the capabilities of existing LLMs and transferring them into a single LLM.
PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection
Drawing inspiration from Psychological Questionnaires, which are carefully designed by psychologists to evaluate individual personality traits through a series of targeted items, we argue that these items can be regarded as a collection of well-structured chain-of-thought (CoT) processes.
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
Instruction-tuning can be substantially optimized through enhanced diversity, resulting in models capable of handling a broader spectrum of tasks.
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System
The results demonstrate that when combined with meta knowledge, the response generator can effectively leverage high-quality knowledge records from the retriever and enhance the quality of generated responses.
Multi-Grained Knowledge Retrieval for End-to-End Task-Oriented Dialog
Retrieving proper domain knowledge from an external database lies at the heart of end-to-end task-oriented dialog systems to generate informative responses.
Clustering-Aware Negative Sampling for Unsupervised Sentence Representation
Contrastive learning has been widely studied in sentence representation learning.
