6 accepted papers
Large language models (LLMs) have recently been applied to time series forecasting to leverage their reasoning and pattern recognition capabilities. Compared to task-specific forecasting models, LLMs exhibit generalizability and a broad understanding of cross-domain knowledge. However, current LLM-b…
Modern language models often rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors. However, they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation, (2) the vast diversity of potential adversa…
In real-world applications, large language models (LLMs) often need to handle diverse and complex instructions. Specifically, when instructions are subject to multiple constraints, some of which are somewhat ambiguous, LLMs often fail to produce answers that satisfy all constraints, limiting their e…
The remarkable performance of Large language models (LLMs) relies heavily on the availability of abundant high-quality training data. However, the high cost of acquiring annotated data often prevents models from obtaining capabilities to tackle downstream tasks. In this paper, we introduce a novel m…
Large language models (LLMs) have demonstrated remarkable evaluation and critique capabilities, providing insightful feedback and identifying flaws in various tasks. However, limited research has explored which types of critiques are most effective for improving model responses or how to generate su…
Estimating optical flow from facial videos is an essential preprocessing step for many applications. However, it is a challenging task as the facial videos contain rich expressions, large displacements, and complex occlusions. Obtaining the ground truth optical flow for facial videos is very difficu…