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Found 14 papers, 3 papers with code
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Data filtering for instruction tuning has proved important in improving both the efficiency and performance of the tuning process.
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.
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.
A Survey on Multi-Behavior Sequential Recommendation
MBSR is a relatively new and worthy direction for in-depth research, which can achieve state-of-the-art recommendation through suitable modeling, and some related works have been proposed.
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point.
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism
Chinese Automatic Speech Recognition (ASR) error correction presents significant challenges due to the Chinese language's unique features, including a large character set and borderless, morpheme-based structure.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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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.
Coil Sketching for computationally-efficient MR iterative reconstruction
Results: First, we performed ablation experiments to validate the sketching matrix design on both Cartesian and non-Cartesian datasets.
Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval
Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated.
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 challenging.
Robust Set Stability of Logic Dynamical Systems with respect to Uncertain Switching
Furthermore, it is proved that, for uniform set stability and asymptotical/finite-time set stability with ratio one, the set stability is equivalent to the stability with respect to the LRIS in the destination set.
An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application
Recently, the artificial intelligence of things (AIoT) has been gaining increasing attention, with an intriguing vision of providing highly intelligent services through the network connection of things, leading to an advanced AI-driven ecology.
Self-Attention for Incomplete Utterance Rewriting
In this paper, we propose a novel method by directly extracting the coreference and omission relationship from the self-attention weight matrix of the transformer instead of word embeddings and edit the original text accordingly to generate the complete utterance.
Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
In closing, we will also share a glimpse into the future of TFX.
