Search Results for author:
Found 9 papers, 8 papers with code
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning
This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs).
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs
Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
Sight Beyond Text: Multi-Modal Training Enhances LLMs in Truthfulness and Ethics
Multi-modal large language models (MLLMs) are trained based on large language models (LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual responses.
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.
ReSee: Responding through Seeing Fine-grained Visual Knowledge in Open-domain Dialogue
To demonstrate the superiority and universality of the provided visual knowledge, we propose a simple but effective framework ReSee to add visual representation into vanilla dialogue models by modality concatenations.
An Overview on Controllable Text Generation via Variational Auto-Encoders
Recent advances in neural-based generative modeling have reignited the hopes of having computer systems capable of conversing with humans and able to understand natural language.
PCAE: A Framework of Plug-in Conditional Auto-Encoder for Controllable Text Generation
Visualization of the local latent prior well confirms the primary devotion in hidden space of the proposed model.
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling
Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time.
