3 accepted papers
Modern deep-learning models have achieved remarkable success in time-series forecasting. Yet, their performance degrades in long-term prediction due to error accumulation in autoregressive inference, where predictions are recursively used as inputs. While classical error correction mechanisms (ECMs)…
Large Language Models (LLMs) exhibit emergent in-context learning (ICL) capabilities, allowing them to adapt to unseen tasks based on example demonstrations. Traditional ICL embeds examples within the prompt, while activation steering, uses a vector derived from examples to guide the latent states o…
Aligning large language models (LLMs) with specific task objectives is challenging, especially when access to feedback signals for guiding the model is limited. While existing parametric methods perform reasonably, they rely heavily on large datasets and frequent feedback, making them impractical in