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Computation & Language / NLP
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Newest papers, page 185
Old Ideas, Novel Problems: The Instability of LLM-Based Novelty Evaluation
Automated ideation systems are often evaluated on the novelty of the ideas they produce, and that judgment is increasingly delegated to large language models.CARM: Cancellation-Aware Response Masking for LLM Reinforcement Learning
Recent years have witnessed the rapid adoption of reinforcement learning (RL) in large language model (LLM) post-training, with substantial gains in mathematical reasoning and code generation.Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages
Some properties of languages, e.g., subject-object-verb (SOV) word order, are more prevalent than others among the thousands of attested natural languages (NLs).LLM-as-Jev: LLMs Are Already Jev-Style Decision Models -- When and How to Fine-Tune Them
Jev-style decision models return categorical probability distributions over predefined options without generating free-form text, enabling software systems to act on their outputs directly.Scalable, Transferable Meta-network for Data Selection Requires a Different Loss (and Why the Obvious Choice is Problematic)
Data selection is critical for training large language models on massive and heterogeneous corpora.A Comparative Explainability Framework for DeBERTa-v3 in Zero-Shot Medical Abstract Classification
A comparative explainability framework is presented to audit DeBERTa-v3 under zero-shot classification of medical abstracts.Where-OPD: Spatially Guided On-Policy Self-Distillation of MLLMs with Synthetic Scenes
On-policy self-distillation has recently emerged as an effective approach for improving language-model reasoning by supervising students with a frozen or EMA version of themselves that receives…Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows
Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on the results.Finetuning with Sampling: SFT Learns Better Than You Think
Introducing new capabilities to frontier models has long been the goal of posttraining, which predominantly employs supervised finetuning (SFT) and reinforcement learning (RL) to this end.Keyword Harnesses Fail Open: A Cheap Diagnostic Ladder for Tool-Use Claims in Small Language Models
Keyword-matching benchmarks can credit small models for tool use they never perform.From Knowledge Access to Source Learning: Developing Source-Specific Competence
Large language model (LLM) agents increasingly rely on persistent external sources to solve sequences of knowledge-intensive tasks.AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents
Coding agents solve repository-level software engineering tasks through long trajectories of code inspection, search, editing, and testing.Every Ablation Is a Dose: Counterweights and the Semblance of Self-Repair
Ablate a component of a language model, and other components often appear to adjust and compensate.Hierarchical Continuous Diffusion Language Models
Discrete diffusion language models offer a compelling alternative to autoregressive generation for tasks demanding bidirectional reasoning and global constraint satisfaction.ScholarCatalyst: A Benchmark for Retrieving Papers That Inspire New Research
What makes great scientists great?KaliBench: A Fine-Grained Benchmark for Cybersecurity Tool Use on Kali Linux with Runtime-Free Verifiable Rewards
LLMs are increasingly applied to cybersecurity workflows, where they are expected to translate analysts' intent into tool invocations.Fast Models, Slow Evidence: A Paired and Self-Audited Evaluation of System-1 Decision Models for LLM Agent Harnesses
Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input carries an injection.HakemBench: A Turkish Benchmark of Typed Decisions
HakemBench is a Turkish benchmark of typed decisions, in which the model under test reads a text, a question and a fixed set of options and returns a probability for every option.Does Every User Need a Private LoRA? Decoupling Personalization from Per-User Adaptation
Personalized large language models often require a complete adaptation state for each user.Lexicographic Multi-Objective On-Policy Distillation
Reinforcement learning from verifiable rewards (RLVR) usually optimizes answer correctness, yet useful language-model behavior also requires high-quality reasoning and concise responses.