LLM

Logic Consistency Makes Large Language Models Personalized Reasoning Teachers
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Logic Consistency Makes Large Language Models Personalized Reasoning Teachers
MADLLM: Multivariate Anomaly Detection via Pre-trained LLMs
When applying pre-trained large language models (LLMs) to address anomaly detection tasks, the multivariate time series (MTS) modality …
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MADLLM: Multivariate Anomaly Detection via Pre-trained LLMs
Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy
Multi-agent systems (MAS) have shown great potential in executing complex tasks, but coordination and safety remain significant …
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Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policy
Cocktail:Chunk-AdaptiveMixed-Precision Ouanization for Long-Context LLM inference
Recently, large language models (LLMs) have been able to handle longer and longer contexts. However, a context that is too long may …
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Cocktail:Chunk-AdaptiveMixed-Precision Ouanization for Long-Context LLM inference
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction …
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Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically …
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QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
In the realm of Large Language Models, the balance between instruction data quality and quantity has become a focal point. Recognizing …
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From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
INCPrompt: Task-Aware Incremental Prompting for Rehearsal-Free Class-incremental Learning
This paper introduces INCPrompt, an innovative continual learning solution that effectively addresses catastrophic forgetting. …
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INCPrompt: Task-Aware Incremental Prompting for Rehearsal-Free Class-incremental Learning
Leveraging Biases in Large Language Models: bias-kNN for Effective Few-Shot Learning
Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning. …
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Leveraging Biases in Large Language Models: bias-kNN for Effective Few-Shot Learning
P2DT: Mitigating Forgetting in Task-Incremental Learning with Progressive Prompt Decision Transformer
Catastrophic forgetting poses a substantial challenge for managing intelligent agents controlled by a large model, causing performance …
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P2DT: Mitigating Forgetting in Task-Incremental Learning with Progressive Prompt Decision Transformer