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Found 17 papers, 13 papers with code
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety.
AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation
This paper studies the vulnerabilities of transformer-based Large Language Models (LLMs) to jailbreaking attacks, focusing specifically on the optimization-based Greedy Coordinate Gradient (GCG) strategy.
VHELM: A Holistic Evaluation of Vision Language Models
Current benchmarks for assessing vision-language models (VLMs) often focus on their perception or problem-solving capabilities and neglect other critical aspects such as fairness, multilinguality, or toxicity.
A Preliminary Study of o1 in Medicine: Are We Closer to an AI Doctor?
Large language models (LLMs) have exhibited remarkable capabilities across various domains and tasks, pushing the boundaries of our knowledge in learning and cognition.
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities.
What If We Recaption Billions of Web Images with LLaMA-3?
For discriminative models like CLIP, we observe enhanced zero-shot performance in cross-modal retrieval tasks.
Ranked #123 on
Visual Question Answering
on MM-Vet
Autoregressive Pretraining with Mamba in Vision
The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks.
How Far Are We From AGI: Are LLMs All We Need?
The evolution of artificial intelligence (AI) has profoundly impacted human society, driving significant advancements in multiple sectors.
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.
