Transformer inference becomes increasingly memory-bound as the Key–Value (KV) cache grows linearly with sequence length. While subquadratic architectures offer constant-memory inference, they rely on aggressive state compression that degrades performance on complex reasoning tasks. We propose Octopus, a framework that confers fixed-memory inference onto pretrained Transformers without the information loss of linearization. Octopus retrofits attention layers with Gated Selective Attention, a learnable module that enforces an adaptive sparsity policy over the context history. By dynamically scoring and retaining only high-utility KV states, this mechanism transforms the unbounded cache into a compact, evolving memory budget that filters out uninformative noise. Empirically, on the GSM8K benchmark, it outperforms state-of-the-art linearized baselines by over 36 points under identical memory constraints. Remarkably, Octopus also surpasses its own full-cache teacher, demonstrating that learned sparse retention serves as an effective regularizer for long-horizon reasoning.

We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into subquadratic architectures. Transformers faces severe computational and memory bottlenecks with long sequences due to the quadratic complexity of softmax attention and the growing Key-Value (KV) cache that makes inference memory-bound by context length. Lizard addresses these limitations by introducing a subquadratic attention mechanism that closely approximates softmax attention while preserving model quality. Unlike prior linearization methods constrained by fixed, non-adaptive structures, Lizard augments the architecture with compact, learnable modules that enable adaptive memory control and robust length generalization. Moreover, we introduce a hardware-aware algorithm that solves numerical instability in gated attention to accelerate training. Extensive experiments show that Lizard achieves near-lossless recovery of its teacher model’s performance, significantly outperforming previous methods by up to 9.4 - 24.5 points on the 5-shot MMLU benchmark and demonstrating superior associative recall.

Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings including on-device, mobile, edge devices, among many others. In this article, we present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques. We propose a novel taxonomy for categorizing the methods used to optimize SLMs, including model compression, pruning, and quantization techniques. We summarize the benchmark datasets that are useful for benchmarking SLMs along with the evaluation metrics commonly used. Additionally, we highlight key open challenges that remain to be addressed. Our survey aims to serve as a valuable resource for researchers and practitioners interested in developing and deploying small yet efficient language models.

We study the problem of Event Causality Identification (ECI) that seeks to predict causal relation between event mentions in the text. In contrast to previous classification-based models, a few recent ECI methods have explored generative models to deliver state-of-the-art performance. However, such generative models cannot handle document-level ECI where long context between event mentions must be encoded to secure correct predictions. In addition, previous generative ECI methods tend to rely on external toolkits or human annotation to obtain necessary training signals. To address these limitations, we propose a novel generative framework that leverages Optimal Transport (OT) to automatically select the most important sentences and words from full documents. Specifically, we introduce hierarchical OT alignments between event pairs and the document to extract pertinent contexts. The selected sentences and words are provided as input and output to a T5 encoder-decoder model which is trained to generate both the causal relation label and salient contexts. This allows richer supervision without external tools. We conduct extensive evaluations on different datasets with multiple languages to demonstrate the benefits and state-of-the-art performance of ECI.

Extensive training datasets represent one of the important factors for the impressive learning capabilities of large language models (LLMs). However, these training datasets for current LLMs, especially the recent state-of-the-art models, are often not fully disclosed. Creating training data for high-performing LLMs involves extensive cleaning and deduplication to ensure the necessary level of quality. The lack of transparency for training data has thus hampered research on attributing and addressing hallucination and bias issues in LLMs, hindering replication efforts and further advancements in the community. These challenges become even more pronounced in multilingual learning scenarios, where the available multilingual text datasets are often inadequately collected and cleaned. Consequently, there is a lack of open-source and readily usable dataset to effectively train LLMs in multiple languages. To overcome this issue, we present CulturaX, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages, tailored for LLM development. Our dataset undergoes meticulous cleaning and deduplication through a rigorous pipeline of multiple stages to accomplish the best quality for model training, including language identification, URL-based filtering, metric-based cleaning, document refinement, and data deduplication. CulturaX is released in Hugging Face facilitate research and advancements in multilingual LLMs: https://huggingface.co/datasets/uonlp/CulturaX.