Classical and neural NLP: translation, question answering, tokenization and the evaluation of language understanding.
arXiv:2609.39645v1 Announce Type: cross Abstract: Multi-agent collaboration lets large language models (LLMs) improve question answering through deliberation and feedback. Yet shared discussion coupl...
GrammarRL introduces a label‑free reinforcement learning approach that adapts language models to grammar constraints without annotated data. It optimizes two self‑supervised rewards—direct and reverse—using a Reinforce Leave‑One‑Out objective over grammar‑constrained rollouts, and regularizes toward a frozen base model. Experiments on sign‑language gloss translation, hierarchical text classification, and named entity recognition with Llama models show consistent gains over constrained greedy decoding and competitive performance to beam search while keeping inference cost low.
arXiv:2604.02543v2 Announce Type: replace Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust thei...
arXiv:2605.27696v3 Announce Type: replace Abstract: Discrete visual tokenizers map images to ordered sequences of tokens, providing a natural representation for structural scene descriptions. Most us...
arXiv:2609.32259v2 Announce Type: replace Abstract: Recent multi-agent LLM systems increasingly combine heterogeneous models for specialized agent roles. However, text-based communication requires ea...
The paper introduces NinaXander, a method for composing frozen language models from different architecture families by inserting a trained shared‑latent adapter between their layers. By running the initial layers of one model, converting the intermediate representation with the adapter, and then continuing with the remaining layers of another model, multiple composed models can be created without retraining. Experiments with RWKV and Pythia show that while some compositions preserve syntactic quality and reduce memory usage, none match the parent model’s accuracy and language‑modeling performance drops on out‑of‑domain data.
arXiv:2609.39001v1 Announce Type: cross Abstract: LLM services are billed per token and context windows are measured in tokens, yet the number of tokens needed for the same content varies across lang...
arXiv:2609.39786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based...
arXiv:2609.39975v1 Announce Type: cross Abstract: This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluat...
The paper introduces a Voice Retention / Representation Ratio metric to assess bias in large language model (LLM) summaries of employee feedback. Using a bilingual corpus of 2,586 responses from a global professional services firm, the study finds that criticism is reported more reliably than praise, and that LLM summaries tend to filter by popularity rather than sentiment—criticism often survives while single-mention concerns, short or German-only content are frequently omitted. The authors argue that prevalence, not sentiment, drives the bias, and provide a metric, field evidence, and a disaggregated voice‑retention card for future audits.
arXiv:2608.03729v4 Announce Type: replace-cross Abstract: Automated Knowledge Base Construction (AKBC) is a core NLP task, and recent work proposes generating knowledge bases directly from large lang...
The paper introduces BARRAC, a method that adapts an English aspect‑based sentiment analysis framework for Arabic dialect classification tasks. It replaces English consumer‑review attribute pools with Arabic linguistic markers for sentiment, sarcasm, and dialect identification, and swaps noisy self‑training for a two‑stage training process. Evaluated on five Arabic dialect datasets, BARRAC achieves a mean macro‑F1 of 63.93%, surpassing the best few‑label state‑of‑the‑art by 3% and outperforming GPT‑4o on four of the five tasks, while error analysis highlights remaining challenges.
Drift Inspector is an open‑source system that extracts Atomic Contribution Claims (ACCs) from scientific abstracts using an LLM, then clusters these claims over time to map how a research field evolves. Applied to six years of EMNLP, the tool reveals a shift from classic NLP tasks toward LLM‑era capabilities such as reasoning and multimodality—trends that keyword or whole‑abstract counts miss. The pipeline has also processed the entire ACL Anthology, yielding 346,000 claims from 80,000 abstracts across 423 venues, with human‑validated extraction and clustering aligned to an external taxonomy.
HalluScoring 2026 is a shared task that evaluates hallucination detection and factual verification in Arabic question answering, focusing on generalization to unseen questions and LLMs. It comprises two main tasks with four subtasks: binary hallucination detection (Subtasks 1.1 and 1.2) and answer verification against six candidates in Islamic and general knowledge domains (Subtasks 2.1 and 2.2). Thirteen teams participated, with the top system achieving AUC‑ROC scores of 0.772 and 0.767 for detection, and 0.882 and 0.857 for verification.
ViLegalExpert is a large-scale Vietnamese legal benchmark built from real citizen–lawyer consultations, comprising over 172,000 questions across 34 legal domains with professional answers and expert-verified evidence. It supports legal information retrieval, extractive QA, and abstractive QA. Experiments show that while pretrained language models perform well on QA, hybrid retrieval methods achieve the best evidence retrieval, highlighting significant challenges in grounding legal answers to authoritative sources.
OP-CAD introduces a curriculum-based, on-policy clean-audio distillation framework that enhances audio-visual reasoning under environmental noise and competing speech. The method trains a student model from mild to severe noise, using a frozen teacher that provides token-level supervision based on clean audio and verified answers, while selectively weighting positions sensitive to acoustic interference. Experiments show OP‑CAD outperforms existing methods across all noise conditions, preserving clean‑correct answers without sacrificing overall accuracy.
The paper introduces CoT-Interpretability Alignment (CIA), a metric that quantifies how well a large language model’s chain-of-thought (CoT) explanations match its internal reasoning processes. Evaluated on two-hop question answering, hint intervention, and integer multiplication across three LLMs, the study finds limited alignment (44.8–75.9%) and demonstrates that post‑training with a reward combining task accuracy and parametric faithfulness can substantially improve CoT faithfulness without sacrificing accuracy. The authors provide a framework for auditing CoT faithfulness and a pathway to making explicit reasoning more trustworthy, with code and data publicly available.
The paper presents a controlled comparison of six retrieval-augmented generation (RAG) strategies for scientific question answering on a large arXiv corpus. All pipelines use the same LLM generator and evaluation protocol, differing only in retrieval design—ranging from classic dense retrieval to late‑interaction methods like ColBERTv2. The authors also release a synthetic question dataset and code to enable reproducible, large‑scale evaluation of RAG trade‑offs.
The paper investigates whether adding Abstract Meaning Representation (AMR) data to large language models (LLMs) improves performance on downstream tasks. By reproducing recent studies and applying a consistent hyperparameter protocol, the authors find that text-only baselines match or surpass AMR-augmented models. A perplexity-based probe shows that AMR does not provide LLMs with additional relational knowledge, suggesting no clear benefit from AMR augmentation.
EvoSteer introduces an online self‑evolving graph orchestration framework that continuously builds and repairs a team of tool‑using agents during execution. It employs Anchored Trajectory Balance (AnchorTB), a regression‑style loss that assigns credit to each orchestration action by comparing subtrajectories to a frozen reference, and Validated Skill Admission, which tests candidate skills before promotion. Experiments on twelve datasets demonstrate that EvoSteer outperforms existing baselines in question answering, mathematical reasoning, code generation, and interactive decision making.