7 accepted papers
Recent advances in fMRI pretraining have significantly improved visual decoding accuracy by leveraging cross-subject neuroimaging datasets. A prevailing strategy aligns individual fMRI signals into a shared feature space using subject-specific adapters, followed by a shared decoder. However, this un
The rising cost of acquiring supervised data has driven significant interest in self-improvement for large language models (LLMs). Straightforward unsupervised signals like majority voting have proven effective in generating pseudo-labels for verifiable tasks, while their applicability to unverifiab…
The recent introduction of OpenAI’s O1/O3 model represents a significant milestone in developing strong reasoning capabilities in Large Language Models (LLMs). By introducing more computational budget during test-time, LLMs have the potential to explore more accurate and higher-quality solutions. Ho…
Facial personalization faces challenges to maintain identity fidelity without disrupting the foundation model's prompt consistency. The mainstream personalization models employ identity embedding to integrate identity information within the attention mechanisms. However, our preliminary findings rev…
Recently, the pretrain-finetune paradigm has become a cornerstone in various deep learning areas. While in general the pre-trained model would promote both effectiveness and efficiency of downstream tasks fine-tuning, studies have shown that not all knowledge acquired during pre-training is benefici
Large language models (LLMs) have shown remarkable capability in numerous tasks and applications. However, fine-tuning LLMs using high-quality datasets under external supervision remains prohibitively expensive. In response, LLM self-improvement approaches have been vibrantly developed recently. The…
Current NLP models appear to be achieving performance comparable to human capabilities on well-established benchmarks. New benchmarks are now necessary to test deeper layers of understanding of natural languages by these models. Blackbird's Language Matrices are a recently developed framework th…