arXiv AI June 8, 2026 By Sercan Karaka\c{s}, Yusuf \c{S}im\c{s}ek

Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification

arXiv:2606. 07479v1 Announce Type: cross Abstract: Turkish idiomatic light verb constructions (LVCs) are challenging for multiword expression processing because they often share the same surface form as fully literal verb-object combinations while functioning as a single, partially idiomatic predicate.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

llms

arXiv Machine Learning

Jun 2

A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization

arXiv:2606. 00230v1 Announce Type: new Abstract: Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epochs.

By Sherin Muckatira, Namrata Shivagunde, Vijeta Deshpande, Anna Rumshisky

llms

arXiv AI

Jun 19

Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models

arXiv:2605. 31393v2 Announce Type: replace-cross Abstract: Sign language translation (SLT) remains constrained by the limited availability of paired sign-video/text corpora and by the heavy-tailed vocabularies typical of real-world datasets.

By Pedro Dal Bianco, Jean Paul Nunes Reinhold, Oscar Stanchi, Facundo Quiroga, Franco Ronchetti, Ulisses Brisolara Corr\^ea

llms fine-tuning efficiency

arXiv AI

Jul 13

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

arXiv:2507. 18043v2 Announce Type: replace-cross Abstract: Inference-time steering methods offer a lightweight alternative to fine-tuning large language models (LLMs) and vision-language models (VLMs) by modifying internal activations at test time without updating model weights.

By Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, Mohit Bansal

llms fine-tuning multimodal safety

arXiv Machine Learning

Aug 5

M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models

arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.

By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y

llms nlp benchmarks safety

arXiv AI

Jun 30

LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

arXiv:2606. 29407v1 Announce Type: cross Abstract: There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).

By Xiao You, Tianwei Yan, Shan Zhao

llms nlp

Hugging Face Trending Papers

Jul 21

Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing

Modern ASR models trained on heterogeneously annotated data treat transcription style (verbatim vs. intended) as an uncontrolled latent variable, causing measurable decoding instability, evaluation confounding (up to 60% of reported WER attributable to style mismatch), and unreliable word-level timing.

fine-tuning multimodal safety

Related stories

A Pre-Training Analogue of Grokking in Language Models: Tracing Delayed Grammatical Generalization

Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models

GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs

M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models

LC-ICL: Label-Guided Contrastive In-Context Learning for Robust Information Extraction

Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing