Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts

The quality of natural language texts in fine-tuning datasets plays a critical role in the performance of generative models, particularly in computational creativity tasks such as poem or song lyric generation. Fluency defects in generated poems significantly reduce their value. However, training texts are often sourced from internet-based platforms without stringent quality control, posing a challenge for data engineers to manage defect levels effectively. To address this issue, we propose the use of automated linguistic anomaly detection to identify and filter out low-quality texts from training datasets for creative models. In this paper, we present a comprehensive comparison of unsupervised and supervised text anomaly detection approaches, utilizing both synthetic and human-labeled datasets. We also introduce the RUPOR dataset, a collection of Russian-language human-labeled poems designed for cross-sentence grammatical error detection, and provide the full evaluation code. Our work aims to empower the community with tools and insights to improve the quality of training datasets for generative models in creative domains.

GASCADE: Grouped Summarization of Adverse Drug Event for Enhanced Cancer Pharmacovigilance

In the realm of cancer treatment, summarizing adverse drug events (ADEs) reported by patients using prescribed drugs is crucial for enhancing pharmacovigilance practices and improving drug-related decision-making. While the volume and complexity of pharmacovigilance data have increased, existing research in this field has predominantly focused on general diseases rather than specifically addressing cancer. This work introduces the task of grouped summarization of adverse drug events reported by multiple patients using the same drug for cancer treatment. To address the challenge of limited resources in cancer pharmacovigilance, we present the MultiLabeled Cancer Adverse Drug Reaction and Summarization (MCADRS) dataset. This dataset includes pharmacovigilance posts detailing patient concerns regarding drug efficacy and adverse effects, along with extracted labels for drug names, adverse drug events, severity, and adversity of reactions, as well as summaries of ADEs for each drug. Additionally, we propose the Grouping and Abstractive Summarization of Cancer Adverse Drug events (GASCADE) framework, a novel pipeline that combines the information extraction capabilities of Large Language Models (LLMs) with the summarization power of the encoder-decoder T5 model. Our work is the first to apply alignment techniques, including advanced algorithms like Direct Preference Optimization, to encoder-decoder models using synthetic datasets for summarization tasks. Through extensive experiments, we demonstrate the superior performance of GASCADE across various metrics, validated through both automated assessments and human evaluations. This multitasking approach enhances drug-related decision-making and fosters a deeper understanding of patient concerns, paving the way for advancements in personalized and responsive cancer care. The code and dataset used in this work are publicly available.

The Aloe Family Recipe for Open and Specialized Healthcare LLMs

Purpose: With advancements in Large Language Models (LLMs) for healthcare, the need arises for competitive open-source models to protect the public interest. This work contributes to the field of open medical LLMs by optimizing key stages of data preprocessing and training, while showing how to improve model safety (through DPO) and efficacy (through RAG). The evaluation methodology used, which includes four different types of tests, defines a new standard for the field. The resultant models, shown to be competitive with the best private alternatives, are released with a permisive license. Methods: Building on top of strong base models like Llama 3.1 and Qwen 2.5, Aloe Beta uses a custom dataset to enhance public data with synthetic Chain of Thought examples. The models undergo alignment with Direct Preference Optimization, emphasizing ethical and policy-aligned performance in the presence of jailbreaking attacks. Evaluation includes close-ended, open-ended, safety and human assessments, to maximize the reliability of results. Results: Recommendations are made across the entire pipeline, backed by the solid performance of the Aloe Family. These models deliver competitive performance across healthcare benchmarks and medical fields, and are often preferred by healthcare professionals. On bias and toxicity, the Aloe Beta models significantly improve safety, showing resilience to unseen jailbreaking attacks. For a responsible release, a detailed risk assessment specific to healthcare is attached to the Aloe Family models. Conclusion: The Aloe Beta models, and the recipe that leads to them, are a significant contribution to the open-source medical LLM field, offering top-of-the-line performance while maintaining high ethical requirements. This work sets a new standard for developing and reporting aligned LLMs in healthcare.

LLAMAPIE: Proactive In-Ear Conversation Assistants

We introduce LlamaPIE, the first real-time proactive assistant designed to enhance human conversations through discreet, concise guidance delivered via hearable devices. Unlike traditional language models that require explicit user invocation, this assistant operates in the background, anticipating user needs without interrupting conversations. We address several challenges, including determining when to respond, crafting concise responses that enhance conversations, leveraging knowledge of the user for context-aware assistance, and real-time, on-device processing. To achieve this, we construct a semi-synthetic dialogue dataset and propose a two-model pipeline: a small model that decides when to respond and a larger model that generates the response. We evaluate our approach on real-world datasets, demonstrating its effectiveness in providing helpful, unobtrusive assistance. User studies with our assistant, implemented on Apple Silicon M2 hardware, show a strong preference for the proactive assistant over both a baseline with no assistance and a reactive model, highlighting the potential of LlamaPie to enhance live conversations.

Breaking Annotation Barriers: Generalized Video Quality Assessment via Ranking-based Self-Supervision

Video quality assessment (VQA) is essential for quantifying perceptual quality in various video processing workflows, spanning from camera capture systems to over-the-top streaming platforms. While recent supervised VQA models have made substantial progress, the reliance on manually annotated datasets -- a process that is labor-intensive, costly, and difficult to scale up -- has hindered further optimization of their generalization to unseen video content and distortions. To bridge this gap, we introduce a self-supervised learning framework for VQA to learn quality assessment capabilities from large-scale, unlabeled web videos. Our approach leverages a \textbf{learning-to-rank} paradigm to train a large multimodal model (LMM) on video pairs automatically labeled via two manners, including quality pseudo-labeling by existing VQA models and relative quality ranking based on synthetic distortion simulations. Furthermore, we introduce a novel \textbf{iterative self-improvement training strategy}, where the trained model acts an improved annotator to iteratively refine the annotation quality of training data. By training on a dataset $10\times$ larger than the existing VQA benchmarks, our model: (1) achieves zero-shot performance on in-domain VQA benchmarks that matches or surpasses supervised models; (2) demonstrates superior out-of-distribution (OOD) generalization across diverse video content and distortions; and (3) sets a new state-of-the-art when fine-tuned on human-labeled datasets. Extensive experimental results validate the effectiveness of our self-supervised approach in training generalized VQA models. The datasets and code will be publicly released to facilitate future research.

Corner Cases: How Size and Position of Objects Challenge ImageNet-Trained Models

Backgrounds in images play a major role in contributing to spurious correlations among different data points. Owing to aesthetic preferences of humans capturing the images, datasets can exhibit positional (location of the object within a given frame) and size (region-of-interest to image ratio) biases for different classes. In this paper, we show that these biases can impact how much a model relies on spurious features in the background to make its predictions. To better illustrate our findings, we propose a synthetic dataset derived from ImageNet1k, Hard-Spurious-ImageNet, which contains images with various backgrounds, object positions, and object sizes. By evaluating the dataset on different pretrained models, we find that most models rely heavily on spurious features in the background when the region-of-interest (ROI) to image ratio is small and the object is far from the center of the image. Moreover, we also show that current methods that aim to mitigate harmful spurious features, do not take into account these factors, hence fail to achieve considerable performance gains for worst-group accuracies when the size and location of core features in an image change.

GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data

Embodied foundation models are gaining increasing attention for their zero-shot generalization, scalability, and adaptability to new tasks through few-shot post-training. However, existing models rely heavily on real-world data, which is costly and labor-intensive to collect. Synthetic data offers a cost-effective alternative, yet its potential remains largely underexplored. To bridge this gap, we explore the feasibility of training Vision-Language-Action models entirely with large-scale synthetic action data. We curate SynGrasp-1B, a billion-frame robotic grasping dataset generated in simulation with photorealistic rendering and extensive domain randomization. Building on this, we present GraspVLA, a VLA model pretrained on large-scale synthetic action data as a foundational model for grasping tasks. GraspVLA integrates autoregressive perception tasks and flow-matching-based action generation into a unified Chain-of-Thought process, enabling joint training on synthetic action data and Internet semantics data. This design helps mitigate sim-to-real gaps and facilitates the transfer of learned actions to a broader range of Internet-covered objects, achieving open-vocabulary generalization in grasping. Extensive evaluations across real-world and simulation benchmarks demonstrate GraspVLA's advanced zero-shot generalizability and few-shot adaptability to specific human preferences. We will release SynGrasp-1B dataset and pre-trained weights to benefit the community.

Improving Failure Prediction in Aircraft Fastener Assembly Using Synthetic Data in Imbalanced Datasets

Automating aircraft manufacturing still relies heavily on human labor due to the complexity of the assembly processes and customization requirements. One key challenge is achieving precise positioning, especially for large aircraft structures, where errors can lead to substantial maintenance costs or part rejection. Existing solutions often require costly hardware or lack flexibility. Used in aircraft by the thousands, threaded fasteners, e.g., screws, bolts, and collars, are traditionally executed by fixed-base robots and usually have problems in being deployed in the mentioned manufacturing sites. This paper emphasizes the importance of error detection and classification for efficient and safe assembly of threaded fasteners, especially aeronautical collars. Safe assembly of threaded fasteners is paramount since acquiring sufficient data for training deep learning models poses challenges due to the rarity of failure cases and imbalanced datasets. The paper addresses this by proposing techniques like class weighting and data augmentation, specifically tailored for temporal series data, to improve classification performance. Furthermore, the paper introduces a novel problem-modeling approach, emphasizing metrics relevant to collar assembly rather than solely focusing on accuracy. This tailored approach enhances the models' capability to handle the challenges of threaded fastener assembly effectively.

PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers

Humans excel in navigating diverse, complex environments with agile motor skills, exemplified by parkour practitioners performing dynamic maneuvers, such as climbing up walls and jumping across gaps. Reproducing these agile movements with simulated characters remains challenging, in part due to the scarcity of motion capture data for agile terrain traversal behaviors and the high cost of acquiring such data. In this work, we introduce PARC (Physics-based Augmentation with Reinforcement Learning for Character Controllers), a framework that leverages machine learning and physics-based simulation to iteratively augment motion datasets and expand the capabilities of terrain traversal controllers. PARC begins by training a motion generator on a small dataset consisting of core terrain traversal skills. The motion generator is then used to produce synthetic data for traversing new terrains. However, these generated motions often exhibit artifacts, such as incorrect contacts or discontinuities. To correct these artifacts, we train a physics-based tracking controller to imitate the motions in simulation. The corrected motions are then added to the dataset, which is used to continue training the motion generator in the next iteration. PARC's iterative process jointly expands the capabilities of the motion generator and tracker, creating agile and versatile models for interacting with complex environments. PARC provides an effective approach to develop controllers for agile terrain traversal, which bridges the gap between the scarcity of motion data and the need for versatile character controllers.

Improving Model Alignment Through Collective Intelligence of Open-Source LLMS

Building helpful and harmless large language models (LLMs) requires effective model alignment approach based on human instructions and feedback, which necessitates high-quality human-labeled data. Constructing such datasets is often expensive and hard to scale, and may face potential limitations on diversity and generalization. To address these challenges, we introduce Mixture of Agents Alignment (MoAA), that leverages the collective strengths of various language models to provide high-quality data for model alignment. By employing MoAA, we enhance both supervised fine-tuning and preference optimization, leading to improved performance compared to using a single model alone to generate alignment data (e.g. using GPT-4o alone). Evaluation results show that our approach can improve win rate of LLaMA-3.1-8B-Instruct from 19.5 to 48.3 on Arena-Hard and from 22.33 to 57.23 on AlpacaEval2, highlighting a promising direction for model alignment through this new scalable and diverse synthetic data recipe. Furthermore, we demonstrate that MoAA enables a self-improvement pipeline, where models finetuned on MoA-generated data surpass their own initial capabilities, providing evidence that our approach can push the frontier of open-source LLMs without reliance on stronger external supervision. Data and code will be released.