Zinan Wang

Haun Leung, ZiNan Wang

Since the emergence of Vision Transformer (ViT), it has been widely used in generative language model and generative visual model. Especially in the current state-of-art open source multimodal models, ViT obtained by CLIP or SigLIP method serves as the vision encoder backbone to help them acquire visual understanding capabilities. But this method leads to limitations in visual understanding for details, such as difficulty in recognizing small text or numbers in images. To address these issues, we propose a new model to unify pix token and word token into the generative language model. The new model also features with each pix of image having its own token embedding, color folding, global conditional attention approximation and image unsupervised pretraining. We conducted image unsupervised pretraining experiments using our new model to explore its potential. The experimental results show that it has good performance even in small model and with limited training data. We believe our model also conforms to the scaling law, as long as model parameters and training data increased, its performance will continue to improve.

Zinan Wang

In the area of code generation research, the emphasis has transitioned from crafting individual functions to developing class-level method code that integrates contextual information. This shift has brought several benchmarks such as ClassEval and CoderEval, which consider class-level contexts. Nevertheless, the influence of specific contextual factors at the method level remains less explored. This research focused on method-level code generation within the Object-Oriented Programming (OOP) framework. Based on CoderEval, we devised experiments that varied the extent of contextual information in the prompts, ranging from method-specific to project-wide details. We introduced the innovative metric of "Prompt-Token Cost-Effectiveness" to evaluate the economic viability of incorporating additional contextual layers. Our findings indicate that prompts enriched with method invocation details yield the highest cost-effectiveness. Additionally, our study revealed disparities among Large Language Models (LLMs) regarding error type distributions and the level of assistance they provide to developers. Notably, larger LLMs do not invariably perform better. We also observed that tasks with higher degrees of coupling present more substantial challenges, suggesting that the choice of LLM should be tailored to the task's coupling degree. For example, GPT-4 exhibited improved performance in low-coupling scenarios, whereas GPT-3.5 seemed better suited for tasks with high coupling. By meticulously curating prompt content and selecting the appropriate LLM, developers can optimize code quality while maximizing cost-efficiency during the development process.

Haun Leung, ZiNan Wang

It is popular lately to train large language models to be used as chat assistants, but in the conversation between the user and the chat assistant, there are prompts, require multi-turns between the chat assistant and the user. However, there are a number of issues with the multi-turns conversation: The response of the chat assistant is prone to errors and can't help users achieve their goals, and as the number of conversation turns increases, the probability of errors will also increase; It is difficult for chat assistant to generate responses with different processes based on actual needs for the same prompt; Chat assistant require the use of tools, but the current approach is not elegant and efficient, and the number of tool calls is limited. The main reason for these issues is that large language models don't have the thinking ability as a human, lack the reasoning ability and planning ability, and lack the ability to execute plans. To solve these issues, we propose a thinking method based on a built-in chain of thought: In the multi-turns conversation, for each user prompt, the large language model thinks based on elements such as chat history, thinking context, action calls, memory and knowledge, makes detailed reasoning and planning, and actions according to the plan. We also explored how the large language model enhances thinking ability through this thinking method: Collect training datasets according to the thinking method and fine tune the large language model through supervised learning; Train a consistency reward model and use it as a reward function to fine tune the large language model using reinforcement learning, and the reinforced large language model outputs according to this way of thinking. Our experimental results show that the reasoning ability and planning ability of the large language model are enhanced, and the issues in the multi-turns conversation are solved.

Simon Sinong Zhan, Yao Liu, Philip Wang et al.

We present Sentinel, the first framework for formally evaluating the physical safety of Large Language Model(LLM-based) embodied agents across the semantic, plan, and trajectory levels. Unlike prior methods that rely on heuristic rules or subjective LLM judgments, Sentinel grounds practical safety requirements in formal temporal logic (TL) semantics that can precisely specify state invariants, temporal dependencies, and timing constraints. It then employs a multi-level verification pipeline where (i) at the semantic level, intuitive natural language safety requirements are formalized into TL formulas and the LLM agent's understanding of these requirements is probed for alignment with the TL formulas; (ii) at the plan level, high-level action plans and subgoals generated by the LLM agent are verified against the TL formulas to detect unsafe plans before execution; and (iii) at the trajectory level, multiple execution trajectories are merged into a computation tree and efficiently verified against physically-detailed TL specifications for a final safety check. We apply Sentinel in VirtualHome and ALFRED, and formally evaluate multiple LLM-based embodied agents against diverse safety requirements. Our experiments show that by grounding physical safety in temporal logic and applying verification methods across multiple levels, Sentinel provides a rigorous foundation for systematically evaluating LLM-based embodied agents in physical environments, exposing safety violations overlooked by previous methods and offering insights into their failure modes.