Large Language Model
Nimbus: Secure and Efficient Two-Party Inference for Transformers
Transformer models have gained significant attention due to their power in machine learning tasks.
ChronoMagic-Bench: A Benchmark for Metamorphic Evaluation of T ext-to-Time-lapse Video Generation
"Chrono" derives from the Greek word "chronos", which means "time". Most of them only assess two dimensions: visual quality and text relevance.
Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios
Conducting experiments and collecting data for machine learning models is a complex and expensive endeavor, particularly when confronted with limited information. Typically, extensive experiments to obtain features and labels come with a significant acquisition cost, making it impractical to carry out all of them. Therefore, it becomes crucial to strategically determine what to acquire to maximize the predictive performance while minimizing costs.
A More Results
The overall performance in MM-NIAH is shown in Tab. 2, which is obtained by averaging the performance across the six tasks in We also provide the performance of each task in Tab. A.1 More findings In addition to the findings discussed in Section 4.2, we provide more findings here. Placing questions before context does NOT improve model performance. Therefore, we do not provide quantitative results but qualitatively analyzed this issue. The long context understanding ability of Gemini-1.5 is not perfect.
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning Bei Li
On the WMT'14 English-German and English-French tasks, our model achieved BLEU scores of 30.95 and 44.27, respectively.