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Computation & Language / NLP
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Newest papers, page 137
zip2zip++: Towards Inference-Time Token Compression with Minimal Quality Loss
Dynamic vocabularies built on-the-fly via Lempel-Ziv-Welch (LZW) compression let a language model represent the same text in fewer tokens, but prior work shows that this efficiency comes at a cost…BLiMPPI: Benchmark of Linguistic Minimal Pairs for Polarity Items
Negative polarity items (NPIs), such as "anymore" and "ever", are grammatical only in select environments, predominantly in the context of negation.Training User Simulators with Latent Intrinsic Feedback
User simulators are gaining attention as environments to train LLM assistants that can better cope with the nuances of multi-turn conversations with real users.APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces
Professional agents increasingly work through command-line interfaces and multiple tools over shared workspaces containing files, messages, and business applications.AMLworld-Compact: Importance-Weighted Downsampling for Cost-Effective LLM Evaluation and Error Diagnosis
Evaluating large language models (LLMs) on AMLworld's smallest test splits is costly because each of over a million targets requires a separate graph prompt.Multi-Stream Attention Residuals
Attention Residuals (AttnRes) enables content-dependent retrieval over network depth, which can be viewed as single-stream softmax attention along the depth axis.A Multi-Agent LLM System for Automated Tuning of Specialized Neural Network Models
Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usually manual.Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction
Large language models struggle to reliably identify and correct errors in their own reasoning.Retractions Revisited: A Replication, Extension, and Mechanistic Evaluation of Linguistic Obfuscation Effects in Fraudulent Science
Prior work suggests fraudulent scientists write their articles with higher rates of linguistic obfuscation than genuine scientists, with a possible mechanism suggesting obfuscation makes research…Verifiable Retrieval for Legal Reasoning through Structure-driven Knowledge Graph
Any automation within reliability-critical contexts such as medicine or law has to be transparent and verifiable.Adaptive DHiSparse: Speculative Decoding over a Load-Adaptive KV Hierarchy
Long-context serving runs out of KV memory before the hardware reaches its useful throughput.Enhancing Legal Reasoning in Pre-trained Language Models via Knowledge Graph-Guided Multi-Task Pre-training
Legal language understanding requires reasoning over semi-structured regulatory documents, yet standard pre-training objectives do not explicitly target this structural capability.A Privileged Teacher That Only Scores Cannot Search: Critique-Conditioned On-Policy Self-Distillation for Reasoning
On-policy self-distillation promises to replace the sparse outcome signal of verifiable-reward RL with dense token-level credit: a teacher conditioned on the reference solution scores the student's…UkrMeme: Multimodal Explainable Persuasion Detection in Ukrainian Memes
Memes play a central role in modern online communication, especially in contexts of political conflict where visual and textual cues combine to convey persuasive messages.OccludeBench: Can VLMs Find What's Missing?
How well can vision-language models (VLMs) reason about what is missing from an image?Not Just an Evaluation: A Case Study of Discourse Particle Sensitivity in Language Models
Discourse particles subtly shape the flow of discourse by reflecting conversational context and interlocutor attitudes, often leaving the truth-conditional meaning untouched.REFLECT: Role-Aware Detection of Sentiment, Emotion, Politeness, and Formality
Detecting communicative meaning in dialogue requires modeling not only lexical content but also the participant-role structures through which sentiment, emotion, politeness, and formality (SEPF) are…Error Rate is Not Enough: Costs of Augmentation in Low-Resource ASR
Data augmentation is standard practice for low-resource automatic speech recognition (ASR), but its benefits are almost always reported as aggregate error rates, leaving open why and when a method…Tracing and Taming Hallucinated Terms via Subsequence-Association Filtering and Breadth-Aware Selection for LLM Query Expansion
Large language models (LLMs) are now a standard source of query expansion terms but they hallucinate.QTestBench: A Test Framework for Evaluating LLM-Generated Quantum Code
Large language models (LLMs) have shown strong results in classical code generation and are increasingly used to write the unit tests that safeguard that code.