Advancing Single- and Multi-task Text Classification through Large Language Model Fine-tuning

Both encoder-only models (e.g., BERT, RoBERTa) and large language models (LLMs, e.g., Llama3) have been widely used for text classification tasks. However, there is a lack of systematic studies comparing the performance of encoder-based models and LLMs in text classification, particularly when fine-tuning is involved. This study employed a diverse range of models and methods, varying in size and architecture, and including both fine-tuned and pre-trained approaches. We first assessed the performances of these LLMs on the 20 Newsgroups (20NG) and MASSIVE datasets, comparing them to encoder-only RoBERTa models. Additionally, we explored the multi-task capabilities of both model types by combining multiple classification tasks, including intent detection and slot-filling, into a single model using data from both datasets. Our results indicate that fully fine-tuned Llama3-70B models outperform RoBERTa-large and other decoder LLMs across various classification tasks and datasets. Moreover, the consolidated multi-task fine-tuned LLMs matched the performance of dual-model setups in both tasks across both datasets. Overall, our study provides a comprehensive benchmark of encoder-only and LLM models on text classification tasks and demonstrates a method to combine two or more fully fine-tuned decoder LLMs for reduced latency and equivalent performance.

Can Open-source LLMs Enhance Data Synthesis for Toxic Detection?: An Experimental Study

Effective toxic content detection relies heavily on high-quality and diverse data, which serves as the foundation for robust content moderation models. This study explores the potential of open-source LLMs for harmful data synthesis, utilizing prompt engineering and fine-tuning techniques to enhance data quality and diversity. In a two-stage evaluation, we first examine the capabilities of six open-source LLMs in generating harmful data across multiple datasets using prompt engineering. In the second stage, we fine-tune these models to improve data generation while addressing challenges such as hallucination, data duplication, and overfitting. Our findings reveal that Mistral excels in generating high-quality and diverse harmful data with minimal hallucination. Furthermore, fine-tuning enhances data quality, offering scalable and cost-effective solutions for augmenting datasets for specific toxic content detection tasks. These results emphasize the significance of data synthesis in building robust, standalone detection models and highlight the potential of open-source LLMs to advance smaller downstream content moderation systems. We implemented this approach in real-world industrial settings, demonstrating the feasibility and efficiency of fine-tuned open-source LLMs for harmful data synthesis.

Can Open-source LLMs Enhance Data Augmentation for Toxic Detection?: An Experimental Study

High-quality, diverse harmful data is essential to addressing real-time applications in content moderation. Current state-of-the-art approaches to toxic content detection using GPT series models are costly and lack explainability. This paper investigates the use of prompt engineering and fine-tuning techniques on open-source LLMs to enhance harmful data augmentation specifically for toxic content detection. We conduct a two-stage empirical study, with stage 1 evaluating six open-source LLMs across multiple datasets using only prompt engineering and stage 2 focusing on fine-tuning. Our findings indicate that Mistral can excel in generating harmful data with minimal hallucination. While fine-tuning these models improves data quality and diversity, challenges such as data duplication and overfitting persist. Our experimental results highlight scalable, cost-effective strategies for enhancing toxic content detection systems. These findings not only demonstrate the potential of open-source LLMs in creating robust content moderation tools. The application of this method in real industrial scenarios further proves the feasibility and efficiency of the fine-tuned open-source LLMs for data augmentation. We hope our study will aid in understanding the capabilities and limitations of current models in toxic content detection and drive further advancements in this field.

Generalizing Motion Planners with Mixture of Experts for Autonomous Driving

Large real-world driving datasets have sparked significant research into various aspects of data-driven motion planners for autonomous driving. These include data augmentation, model architecture, reward design, training strategies, and planner pipelines. These planners promise better generalizations on complicated and few-shot cases than previous methods. However, experiment results show that many of these approaches produce limited generalization abilities in planning performance due to overly complex designs or training paradigms. In this paper, we review and benchmark previous methods focusing on generalizations. The experimental results indicate that as models are appropriately scaled, many design elements become redundant. We introduce StateTransformer-2 (STR2), a scalable, decoder-only motion planner that uses a Vision Transformer (ViT) encoder and a mixture-of-experts (MoE) causal Transformer architecture. The MoE backbone addresses modality collapse and reward balancing by expert routing during training. Extensive experiments on the NuPlan dataset show that our method generalizes better than previous approaches across different test sets and closed-loop simulations. Furthermore, we assess its scalability on billions of real-world urban driving scenarios, demonstrating consistent accuracy improvements as both data and model size grow.

Playful DoggyBot: Learning Agile and Precise Quadrupedal Locomotion

Quadrupedal animals have the ability to perform agile while accurate tasks: a trained dog can chase and catch a flying frisbee before it touches the ground; a cat alone at home can jump and grab the door handle accurately. However, agility and precision are usually a trade-off in robotics problems. Recent works in quadruped robots either focus on agile but not-so-accurate tasks, such as locomotion in challenging terrain, or accurate but not-so-fast tasks, such as using an additional manipulator to interact with objects. In this work, we aim at an accurate and agile task, catching a small object hanging above the robot. We mount a passive gripper in front of the robot chassis, so that the robot has to jump and catch the object with extreme precision. Our experiment shows that our system is able to jump and successfully catch the ball at 1.05m high in simulation and 0.8m high in the real world, while the robot is 0.3m high when standing.

Seed-Music: A Unified Framework for High Quality and Controlled Music Generation

We introduce Seed-Music, a suite of music generation systems capable of producing high-quality music with fine-grained style control. Our unified framework leverages both auto-regressive language modeling and diffusion approaches to support two key music creation workflows: \textit{controlled music generation} and \textit{post-production editing}. For controlled music generation, our system enables vocal music generation with performance controls from multi-modal inputs, including style descriptions, audio references, musical scores, and voice prompts. For post-production editing, it offers interactive tools for editing lyrics and vocal melodies directly in the generated audio. We encourage readers to listen to demo audio examples at https://team.doubao.com/seed-music .

Robust Robot Walker: Learning Agile Locomotion over Tiny Traps

Quadruped robots must exhibit robust walking capabilities in practical applications. In this work, we propose a novel approach that enables quadruped robots to pass various small obstacles, or "tiny traps". Existing methods often rely on exteroceptive sensors, which can be unreliable for detecting such tiny traps. To overcome this limitation, our approach focuses solely on proprioceptive inputs. We introduce a two-stage training framework incorporating a contact encoder and a classification head to learn implicit representations of different traps. Additionally, we design a set of tailored reward functions to improve both the stability of training and the ease of deployment for goal-tracking tasks. To benefit further research, we design a new benchmark for tiny trap task. Extensive experiments in both simulation and real-world settings demonstrate the effectiveness and robustness of our method. Project Page: https://robust-robot-walker.github.io/

Cross Anything: General Quadruped Robot Navigation through Complex Terrains

The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks, but there are few explorations for foundation models used in quadruped robot navigation. We introduce Cross Anything System (CAS), an innovative system composed of a high-level reasoning module and a low-level control policy, enabling the robot to navigate across complex 3D terrains and reach the goal position. For high-level reasoning and motion planning, we propose a novel algorithmic system taking advantage of a VLM, with a design of task decomposition and a closed-loop sub-task execution mechanism. For low-level locomotion control, we utilize the Probability Annealing Selection (PAS) method to train a control policy by reinforcement learning. Numerous experiments show that our whole system can accurately and robustly navigate across complex 3D terrains, and its strong generalization ability ensures the applications in diverse indoor and outdoor scenarios and terrains. Project page: https://cross-anything.github.io/

Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation

CLIP, as a vision-language model, has significantly advanced Open-Vocabulary Semantic Segmentation (OVSS) with its zero-shot capabilities. Despite its success, its application to OVSS faces challenges due to its initial image-level alignment training, which affects its performance in tasks requiring detailed local context. Our study delves into the impact of CLIP's [CLS] token on patch feature correlations, revealing a dominance of "global" patches that hinders local feature discrimination. To overcome this, we propose CLIPtrase, a novel training-free semantic segmentation strategy that enhances local feature awareness through recalibrated self-correlation among patches. This approach demonstrates notable improvements in segmentation accuracy and the ability to maintain semantic coherence across objects.Experiments show that we are 22.3% ahead of CLIP on average on 9 segmentation benchmarks, outperforming existing state-of-the-art training-free methods.The code are made publicly available at: https://github.com/leaves162/CLIPtrase.

DISCO: Efficient Diffusion Solver for Large-Scale Combinatorial Optimization Problems

Combinatorial Optimization (CO) problems are fundamentally crucial in numerous practical applications across diverse industries, characterized by entailing enormous solution space and demanding time-sensitive response. Despite significant advancements made by recent neural solvers, their limited expressiveness does not conform well to the multi-modal nature of CO landscapes. While some research has pivoted towards diffusion models, they require simulating a Markov chain with many steps to produce a sample, which is time-consuming and does not meet the efficiency requirement of real applications, especially at scale. We propose DISCO, an efficient DIffusion Solver for Combinatorial Optimization problems that excels in both solution quality and inference speed. DISCO's efficacy is two-pronged: Firstly, it achieves rapid denoising of solutions through an analytically solvable form, allowing for direct sampling from the solution space with very few reverse-time steps, thereby drastically reducing inference time. Secondly, DISCO enhances solution quality by restricting the sampling space to a more constrained, meaningful domain guided by solution residues, while still preserving the inherent multi-modality of the output probabilistic distributions. DISCO achieves state-of-the-art results on very large Traveling Salesman Problems with 10000 nodes and challenging Maximal Independent Set benchmarks, with its per-instance denoising time up to 44.8 times faster. Through further combining a divide-and-conquer strategy, DISCO can be generalized to solve arbitrary-scale problem instances off the shelf, even outperforming models trained specifically on corresponding scales.