5 accepted papers
While humans develop core visual skills long before acquiring language, contemporary Multimodal LLMs (MLLMs) still rely heavily on linguistic priors to compensate for their fragile visual understanding. We uncovered a crucial fact: state-of-the-art MLLMs consistently fail on basic visual tasks that …
Code evaluation and reinforcement learning rely critically on test cases. However, collecting golden test cases is hard and expensive, motivating the use of LLMs for automatic test case generation. This, in turn, raises a pivotal challenge: how can we rigorously evaluate the quality of the generated…
Recently, due to the explosive advances of large language models (LLMs) on English, cross-lingual continual pretraining has been widely applied in obtaining Chinese LLMs. However, previous studies showed that these LLMs have suffered severe hallucinations, mainly caused by noisy tokens. To this aim,…
Code LLMs have been widely used in various domains, including code generation, logical reasoning, and agent systems. However, open-access code LLMs mostly only release weights, lacking key features such as reproducible data pipelines and transparent training protocols, which are crucial for advancin…
Large Language Models (LLMs) have attained the impressive capability to resolve a wide range of NLP tasks by fine-tuning high-quality instruction data. However, collecting human-written data of high quality, especially multi-turn dialogues, is expensive and unattainable for most people. Though previ…