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Found 9 papers, 2 papers with code
Prejudice and Caprice: A Statistical Framework for Measuring Social Discrimination in Large Language Models
The growing integration of large language models (LLMs) into social operations amplifies their impact on decisions in crucial areas such as economics, law, education, and healthcare, raising public concerns about these models' discrimination-related safety and reliability.
HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models
We further present a strong baseline method HandDiffuse for the controllable motion generation of interacting hands using various controllers.
Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study
This research paper addresses the critical challenge of accurately valuing post-revenue drug assets in the biotechnology and pharmaceutical sectors, a key factor influencing a wide range of strategic operations and investment decisions.
Tuna: Instruction Tuning using Feedback from Large Language Models
Furthermore, we apply probabilistic ranking and contextual ranking sequentially to the instruction-tuned LLM.
TRM-UAP: Enhancing the Transferability of Data-Free Universal Adversarial Perturbation via Truncated Ratio Maximization
Aiming at crafting a single universal adversarial perturbation (UAP) to fool CNN models for various data samples, universal attack enables a more efficient and accurate evaluation for the robustness of CNN models.
Momentum Calibration for Text Generation
The input and output of most text generation tasks can be transformed to two sequences of tokens and they can be modeled using sequence-to-sequence learning modeling tools such as Transformers.
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Does Debiasing Inevitably Degrade the Model Performance
We use our theoretical framework to explain why the current debiasing methods cause performance degradation.
Knowledge is Power: Understanding Causality Makes Legal judgment Prediction Models More Generalizable and Robust
To validate our theoretical analysis, we further propose another method using our proposed Causality-Aware Self-Attention Mechanism (CASAM) to guide the model to learn the underlying causality knowledge in legal texts.
Swin Transformer coupling CNNs Makes Strong Contextual Encoders for VHR Image Road Extraction
While CNN excels at extracting local detail features, the Transformer naturally perceives global contextual information.
