Unlike the existing image-text similarity objective which only categorizes matched pairs as similar and unmatched pairs as dissimilar, equivariance also requires similarity to vary faithfully according to the semantic changes.
In such attacks, an adversary can prompt the LLM to produce malicious content or override the original instructions and the employed filtering schemes.
Nonetheless, we note that only a small fraction of adversarial-crafted perturbations on MGTs can evade the ChatGPT Detector, thus highlighting the need for more robust MGT detection methods.
We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters.
Ranked #1 on Question Answering on PIQA
Applying our method to vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT_001, which is trained with private user data and human annotations.
To address these challenges, we introduce a system that can jointly optimize distributed execution and gradient checkpointing plans.
Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed.
To achieve full automation, we introduce a straightforward yet effective heuristic that enables the agent to pinpoint hallucination instances, avoid repetition in action sequences, and, in some environments, construct an internal memory map of the given environment.
Our generative approach to classification attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models.
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on Language Modelling on CLUE (CMRC2018)