Large Language Model
CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns
The stable periodic patterns present in time series data serve as the foundation for conducting long-horizon forecasts. In this paper, we pioneer the exploration of explicitly modeling this periodicity to enhance the performance of models in long-term time series forecasting (L TSF) tasks. Specifically, we introduce the Residual Cycle Forecasting (RCF) technique, which utilizes learnable recurrent cycles to model the inherent periodic patterns within sequences, and then performs predictions on the residual components of the modeled cycles.
RedCode: Risky Code Execution and Generation Benchmark for Code Agents
We then map the Python code to other programming languages (e.g., Bash) and natural text summaries or descriptions for
Lean Workbook: A large-scale Lean problem set formalized from natural language math problems
I do believe that problems are the heart of mathematics.
Large Language Model Unlearning
We study how to perform unlearning, i.e. forgetting undesirable (mis)behaviors, on large language models (LLMs).
Diffusion of Thought: Chain-of-Thought Reasoning in Diffusion Language Models Jiacheng Y e
Recently, diffusion models have garnered significant interest in the field of text processing due to their many potential advantages compared to conventional autoregressive models.
The PRISM Alignment Dataset: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models Hannah Rose Kirk 1
Human feedback is central to the alignment of Large Language Models (LLMs).
TAIA: Large Language Models are Out-of-Distribution Data Learners
However, in certain specialized domains, such as healthcare or harmless content generation, it is nearly impossible to obtain a large volume of high-quality data that matches the downstream distribution.