LLM

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
Prompt Guided Copy Mechanism for Conversational Question Answering
Conversational Question Answering (CQA) is a challenging task that aims to generate natural answers for conversational flow questions. …
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Prompt Guided Copy Mechanism for Conversational Question Answering
Self-Attention for Incomplete Utterance Rewriting
Incomplete utterance rewriting (IUR) has recently become an essential task in NLP, aiming to complement the incomplete utterance with …
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Self-Attention for Incomplete Utterance Rewriting
VU-BERT: A Unified Framework for Visual Dialog
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Self-supervised Learning for Semantic Sentence Matching with Dense Transformer Inference Network
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Semantic Extraction for Sentence Representation via Reinforcement Learning
Many modern Natural Language Processing(NLP) systems rely on word embeddings, previously trained in an unsupervised manner on large …
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