Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.
arXiv:2605.28042v2 Announce Type: replace-cross Abstract: Modern large language models (LLMs) achieve state-of-the-art machine translation performance, but they do so as broad generalists largely tra...
arXiv:2511.20022v3 Announce Type: replace-cross Abstract: Recent advancements in multimodal large language models (MLLMs) have shown strong understanding of driving scenes, drawing interest in their...
The paper investigates whether language models can reason across languages by introducing a two‑hop question answering task that requires inference over two multilingual documents. Results show that models are more sensitive to language variation in answer‑span documents than in bridging documents, and that up to 33% of multilingual cases involve correct final answers despite failing to infer bridging information in the first step. The study also reveals an 18% composition failure rate and proposes a three‑stage SUBQ prompting method that improves accuracy from 10.1% to 66.5%.
The paper reports on building a retrieval‑augmented legal assistant for Uzbek that operates in both a managed cloud service and an on‑premises deployment. It introduces two new domain benchmarks—one for retrieval and one for end‑to‑end QA—and shows that fine‑tuning an open‑weight text embedder (UTE‑1) can close the performance gap with proprietary models under tight cost and latency constraints. The authors also provide negative results for a QLoRA experiment and release the benchmarks, evaluation code, and the fine‑tuned embedder for future low‑resource legal NLP work.
arXiv:2608.29027v1 Announce Type: new Abstract: Shrimp disease classification has become an urgent issue due to its significant impact on the import-export output of producing countries, particularly...
arXiv:2608.29624v1 Announce Type: new Abstract: Natural Language Processing methods have enabled novel solutions and advances in the field of privacy, particularly in the sub-domain of text-to-text p...
arXiv:2601.12868v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly operate in high-stakes settings where demographic attributes such as race and ethnicity may be expl...
General-purpose NLP embedding models perform well on linguistic tasks, but their ability to capture symbolic ontological structure remains unclear. We introduce AVA, a systematic framework for evaluat...
We present Qwen-Drive-1.0, an initial step towards a vision-language foundation model for autonomous driving. Qwen-Drive-1.0 retains the architecture of the pretrained vision-language model (VLM) and...
Artificial intelligence (AI) and natural language processing (NLP) are increasingly used to extract, integrate, and interpret biomedical knowledge relevant to cancer genomics, yet their translation in...
Building language technologies and conducting NLP research for low-resource languages---particularly when led by native speakers or involving participatory research practices---are often framed as mea...
Large language models (LLMs) are built from structured high-dimensional objects such as token representations, weights, adaptation updates, caches, and activations, whose multilinear structure is unde...
We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, covering spoken visual question answering and image-gro...
Indirect speech acts (ISAs) require pragmatic reasoning over context, as directive intent can- not be inferred from surface form alone. Prior text-based studies and existing multimodal benchmarks larg...
Long-context LLM applications such as document summarization and multi-turn agents require generation from prefixes spanning tens of thousands of tokens, making decoding latency a major bottleneck. Sp...
D-TAIA is a framework that adapts large language models for multi‑task predictive process monitoring, jointly predicting the next activity and remaining time of ongoing cases. It uses domain‑aware triplet loss pre‑training, FAISS‑based nearest‑neighbor retrieval for time estimation, and a TAIA inference strategy to preserve sequential reasoning while fine‑tuning a 10 M‑parameter backbone. Across four real‑world event logs, D‑TAIA achieves state‑of‑the‑art or competitive results compared to a fine‑tuned LLM and a recurrent neural network baseline, with ablation studies showing the effectiveness of NLP and computer‑vision techniques for this domain.
The paper introduces Short‑Films 20K (SF20K), a large publicly available movie dataset comprising 20,143 amateur films totaling 3,582 hours, with an average length of 12 minutes per film. Accompanying the dataset is SF20K‑Test, a manual open‑ended question‑answering benchmark featuring 95 movies and 979 question‑answer pairs. Analysis of the benchmark shows limited data leakage, highlights the necessity of long‑term reasoning, and demonstrates that instruction tuning on the large‑scale dataset significantly boosts vision‑language model performance.
arXiv:2305.12474v4 Announce Type: replace Abstract: Large Language Models(LLMs) have demonstrated remarkable performance across various natural language processing tasks; however, how to comprehensiv...
The paper introduces Active Testing, a framework that selects the most informative test samples for annotation in NLP, aiming to reduce human effort while accurately estimating model performance. Experiments across 18 datasets and 4 embedding strategies show up to 95% annotation savings with less than 1% loss in performance estimation accuracy. The authors also propose an adaptive stopping criterion to determine the optimal number of samples without a predefined budget.
arXiv:2608.27971v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) multimodal perception integrates visible (RGB), infrared (IR), synthetic aperture radar (SAR), and depth sensors for scen...