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Found 9 papers, 4 papers with code
Improving Conversational Recommendation Systems’ Quality with Context-Aware Item Meta-Information
In this paper, we propose a simple yet effective architecture comprising a pre-trained language model (PLM) and an item metadata encoder to integrate the recommendation and the dialog generation better.
SnapKV: LLM Knows What You are Looking for Before Generation
Specifically, SnapKV achieves a consistent decoding speed with a 3. 6x increase in generation speed and an 8. 2x enhancement in memory efficiency compared to baseline when processing inputs of 16K tokens.
BTLM-3B-8K: 7B Parameter Performance in a 3B Parameter Model
BTLM-3B-8K is available under an Apache 2. 0 license on Hugging Face: https://huggingface. co/cerebras/btlm-3b-8k-base.
Rethinking Imitation-based Planner for Autonomous Driving
In recent years, imitation-based driving planners have reported considerable success.
ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles
Automatically generating textual content with desired attributes is an ambitious task that people have pursued long.
Improving Conversational Recommendation Systems' Quality with Context-Aware Item Meta Information
The encoder learns to map item metadata to embeddings that can reflect the semantic information in the dialog context.
AllWOZ: Towards Multilingual Task-Oriented Dialog Systems for All
A commonly observed problem of the state-of-the-art natural language technologies, such as Amazon Alexa and Apple Siri, is that their services do not extend to most developing countries' citizens due to language barriers.
Few-Shot Domain Expansion for Face Anti-Spoofing
In practice, given a handful of labeled samples from a new deployment scenario (target domain) and abundant labeled face images in the existing source domain, the FAS system is expected to perform well in the new scenario without sacrificing the performance on the original domain.
PipeMare: Asynchronous Pipeline Parallel DNN Training
Pipeline parallelism (PP) when training neural networks enables larger models to be partitioned spatially, leading to both lower network communication and overall higher hardware utilization.
