Relative Overfitting and Accept-Reject Framework

Currently, the scaling law of Large Language Models (LLMs) faces challenges and bottlenecks. This paper posits that noise effects, stemming from changes in the signal-to-noise ratio under diminishing marginal returns, are the root cause of these issues. To control this noise, we investigated the differences between models with performance advantages and disadvantages, introducing the concept of "relative overfitting." Based on their complementary strengths, we have proposed an application framework, Accept-Reject (AR). In Natural Language Processing (NLP), we use LLMs and Small Language Models (SLMs) as the medium for discussion. This framework enables SLMs to exert a universal positive influence on LLM decision outputs, rather than the intuitively expected negative influence. We validated our approach using self-built models based on mainstream architectures and pre-trained mainstream models across multiple datasets, including basic language modeling, long-context tasks, subject examination, and question-answering (QA) benchmarks. The results demonstrate that through our structure, compared to increasing the LLM's parameters, we can achieve better performance improvements with significantly lower parameter and computational costs in many scenarios. These improvements are universal, stable, and effective. Furthermore, we explore the potential of "relative overfitting" and the AR framework in other machine learning domains, such as computer vision (CV) and AI for science. We hope the proposed approach can help scale laws overcome existing bottlenecks.

Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases

Large Vision-Language Models (LVLMs), due to the remarkable visual reasoning ability to understand images and videos, have received widespread attention in the autonomous driving domain, which significantly advances the development of interpretable end-to-end autonomous driving. However, current evaluations of LVLMs primarily focus on the multi-faceted capabilities in common scenarios, lacking quantifiable and automated assessment in autonomous driving contexts, let alone severe road corner cases that even the state-of-the-art autonomous driving perception systems struggle to handle. In this paper, we propose CODA-LM, a novel vision-language benchmark for self-driving, which provides the first automatic and quantitative evaluation of LVLMs for interpretable autonomous driving including general perception, regional perception, and driving suggestions. CODA-LM utilizes the texts to describe the road images, exploiting powerful text-only large language models (LLMs) without image inputs to assess the capabilities of LVLMs in autonomous driving scenarios, which reveals stronger alignment with human preferences than LVLM judges. Experiments demonstrate that even the closed-sourced commercial LVLMs like GPT-4V cannot deal with road corner cases well, suggesting that we are still far from a strong LVLM-powered intelligent driving agent, and we hope our CODA-LM can become the catalyst to promote future development.