LLM

Cocktait:Chunk-AdaptiveMixed-PrecisionOuanization for Long-Context LLM inference
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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
On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models
Deep neural retrieval models have amply demonstrated their power but estimating the reliability of their predictions remains …
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On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models
PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter
The Retrieval Question Answering (ReQA) task employs the retrieval-augmented framework, composed of a retriever and generator. The …
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PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter
Sparks of Large Audio Models: A Survey and Outlook
This survey paper provides a comprehensive overview of the recent advancements and challenges in applying large language models to the …
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Sparks of Large Audio Models: A Survey and Outlook