Ubiquitous Field Transportation Robots with Robust Wheel-Leg Transformable Modules

This paper introduces two field transportation robots. Both robots are equipped with transformable wheel-leg modules, which can smoothly switch between operation modes and can work in various challenging terrains. SWhegPro, with six S-shaped legs, enables transporting loads in challenging uneven outdoor terrains. SWhegPro3, featuring four three-impeller wheels, has surprising stair-climbing performance in indoor scenarios. Different from ordinary gear-driven transformable mechanisms, the modular wheels we designed driven by self-locking electric push rods can switch modes accurately and stably with high loads, significantly improving the load capacity of the robot in leg mode. This study analyzes the robot's wheel-leg module operation when the terrain parameters change. Through the derivation of mathematical models and calculations based on simplified kinematic models, a method for optimizing the robot parameters and wheel-leg structure parameters is finally proposed.The design and control strategy are then verified through simulations and field experiments in various complex terrains, and the working performance of the two field transportation robots is calculated and analyzed by recording sensor data and proposing evaluation methods.

Symbolic Working Memory Enhances Language Models for Complex Rule Application

Large Language Models (LLMs) have shown remarkable reasoning performance but struggle with multi-step deductive reasoning involving a series of rule application steps, especially when rules are presented non-sequentially. Our preliminary analysis shows that while LLMs excel in single-step rule application, their performance drops significantly in multi-step scenarios due to the challenge in rule grounding. It requires anchoring the applicable rule and supporting facts at each step, amidst multiple input rules, facts, and inferred facts. To address this, we propose augmenting LLMs with external working memory and introduce a neurosymbolic framework for rule application. The memory stores facts and rules in both natural language and symbolic forms, enabling precise tracking. Utilizing this memory, our framework iteratively performs symbolic rule grounding and LLM-based rule implementation. The former matches predicates and variables of symbolic rules and facts to ground applicable rules at each step. Experiments indicate our framework's effectiveness in rule application and its robustness across various steps and settings~\footnote{Code and data are available at \url{https://github.com/SiyuanWangw/RuleApplication}.}.

Identity-Driven Hierarchical Role-Playing Agents

Utilizing large language models (LLMs) to achieve role-playing has gained great attention recently. The primary implementation methods include leveraging refined prompts and fine-tuning on role-specific datasets. However, these methods suffer from insufficient precision and limited flexibility respectively. To achieve a balance between flexibility and precision, we construct a Hierarchical Identity Role-Playing Framework (HIRPF) based on identity theory, constructing complex characters using multiple identity combinations. We develop an identity dialogue dataset for this framework and propose an evaluation benchmark including scale evaluation and open situation evaluation. Empirical results indicate the remarkable efficacy of our framework in modeling identity-level role simulation, and reveal its potential for application in social simulation.

Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks

Recent advancements in Large Language Models (LLMs) have led to significant breakthroughs in various natural language processing tasks. However, generating factually consistent responses in knowledge-intensive scenarios remains a challenge due to issues such as hallucination, difficulty in acquiring long-tailed knowledge, and limited memory expansion. This paper introduces SMART, a novel multi-agent framework that leverages external knowledge to enhance the interpretability and factual consistency of LLM-generated responses. SMART comprises four specialized agents, each performing a specific sub-trajectory action to navigate complex knowledge-intensive tasks. We propose a multi-agent co-training paradigm, Long- and Short-Trajectory Learning, which ensures synergistic collaboration among agents while maintaining fine-grained execution by each agent. Extensive experiments on 5 tasks demonstrate SMART's superior performance compared to previous widely adopted methods.

HAF-RM: A Hybrid Alignment Framework for Reward Model Training

The reward model has become increasingly important in alignment, assessment, and data construction for large language models (LLMs). Most existing researchers focus on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards. In this paper, we propose a hybrid alignment framework HaF-RM for reward model training by introducing an additional constraint on token-level policy probabilities in addition to the reward score. It can simultaneously supervise the internal preference model at the token level and optimize the mapping layer of the reward model at the sequence level. Theoretical justifications and experiment results on five datasets show the validity and effectiveness of our proposed hybrid framework for training a high-quality reward model. By decoupling the reward modeling procedure and incorporating hybrid supervision, our HaF-RM framework offers a principled and effective approach to enhancing the performance and alignment of reward models, a critical component in the responsible development of powerful language models. We release our code at https://haf-rm.github.io.

From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking

The rapid development of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has exposed vulnerabilities to various adversarial attacks. This paper provides a comprehensive overview of jailbreaking research targeting both LLMs and MLLMs, highlighting recent advancements in evaluation benchmarks, attack techniques and defense strategies. Compared to the more advanced state of unimodal jailbreaking, multimodal domain remains underexplored. We summarize the limitations and potential research directions of multimodal jailbreaking, aiming to inspire future research and further enhance the robustness and security of MLLMs.

ALaRM: Align Language Models via Hierarchical Rewards Modeling

We introduce ALaRM, the first framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF), which is designed to enhance the alignment of large language models (LLMs) with human preferences. The framework addresses the limitations of current alignment approaches, which often struggle with the inconsistency and sparsity of human supervision signals, by integrating holistic rewards with aspect-specific rewards. This integration enables more precise and consistent guidance of language models towards desired outcomes, particularly in complex and open text generation tasks. By employing a methodology that filters and combines multiple rewards based on their consistency, the framework provides a reliable mechanism for improving model alignment. We validate our approach through applications in long-form question answering and machine translation tasks, employing gpt-3.5-turbo for pairwise comparisons, and demonstrate improvements over existing baselines. Our work underscores the effectiveness of hierarchical rewards modeling in refining LLM training processes for better human preference alignment. We release our code at https://ALaRM-fdu.github.io.

Aligning Large Language Models to a Domain-specific Graph Database

Graph Databases (Graph DB) are widely applied in various fields, including finance, social networks, and medicine. However, translating Natural Language (NL) into the Graph Query Language (GQL), commonly known as NL2GQL, proves to be challenging due to its inherent complexity and specialized nature. Some approaches have sought to utilize Large Language Models (LLMs) to address analogous tasks like text2SQL. Nevertheless, when it comes to NL2GQL taskson a particular domain, the absence of domain-specific NL-GQL data pairs makes it difficult to establish alignment between LLMs and the graph DB. To address this challenge, we propose a well-defined pipeline. Specifically, we utilize ChatGPT to create NL-GQL data pairs based on the given graph DB with self-instruct. Then, we use the created data to fine-tune LLMs, thereby achieving alignment between LLMs and the graph DB. Additionally, during inference, we propose a method that extracts relevant schema to the queried NL as the input context to guide LLMs for generating accurate GQLs.We evaluate our method on two constructed datasets deriving from graph DBs in finance domain and medicine domain, namely FinGQL and MediGQL. Experimental results demonstrate that our method significantly outperforms a set of baseline methods, with improvements of 5.90 and 6.36 absolute points on EM, and 6.00 and 7.09 absolute points on EX, respectively.

AI Hospital: Interactive Evaluation and Collaboration of LLMs as Intern Doctors for Clinical Diagnosis

The incorporation of Large Language Models (LLMs) in healthcare marks a significant advancement. However, the application has predominantly been limited to discriminative and question-answering tasks, which does not fully leverage their interactive potential. To address this limitation, our paper presents AI Hospital, a framework designed to build a real-time interactive diagnosis environment. To simulate the procedure, we collect high-quality medical records to create patient, examiner, and medical director agents. AI Hospital is then utilized for the interactive evaluation and collaboration of LLMs. Initially, we create a Multi-View Medical Evaluation (MVME) benchmark where various LLMs serve as intern doctors for interactive diagnosis. Subsequently, to improve diagnostic accuracy, we introduce a collaborative mechanism that involves iterative discussions and a dispute resolution process under the supervision of the medical director. In our experiments, we validate the reliability of AI Hospital. The results not only explore the feasibility of apply LLMs in clinical consultation but also confirm the effectiveness of the dispute resolution focused collaboration method.

Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMs

Large language models (LLMs) have achieved impressive human-like performance across various reasoning tasks. However, their mastery of underlying inferential rules still falls short of human capabilities. To investigate this, we propose a logic scaffolding inferential rule generation framework, to construct an inferential rule base, ULogic, comprising both primitive and compositional rules across five domains. Our analysis of GPT-series models over a rule subset reveals significant gaps in LLMs' logic understanding compared to human performance, especially in compositional and structural complex rules with certain bias patterns. We further distill these rules into a smaller-scale inference engine for flexible rule generation and enhancing downstream reasoning. Through a multi-judger evaluation, our inference engine proves effective in generating accurate, complex and abstract conclusions and premises, and improve various commonsense reasoning tasks. Overall, our work sheds light on LLMs' limitations in grasping inferential rule and suggests ways to enhance their logical reasoning abilities~\footnote{Code and data are available at \url{https://github.com/SiyuanWangw/ULogic}.}.