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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. 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 Machine Learning

Sep 1

Cross-lingual Functional Vectors for Emotion Detection in Large Language Models

arXiv:2608.29613v1 Announce Type: cross Abstract: Function vectors (FVs) have recently emerged as a promising mechanism for steering the behavior of large language models (LLMs) by injecting task-spe...

By Jieying Xue, Phuong Minh Nguyen, Minh Le Nguyen, Shogo Okada

llms benchmarks

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 Computation and Language

Sep 10

Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models

arXiv:2606.05087v2 Announce Type: replace Abstract: Frequent verbs such as 'have' and 'make' can function either as collocates in light-verb constructions or as full lexical predicates, as in 'make a...

By Francesca Franzon, Nicolas Ros\`as G\'omez, Leo Wanner

llms

arXiv Computation and Language

Sep 4

To What Extent Do Large Language Models Understand Bangla Idioms?

The paper introduces the first large‑scale benchmark dataset of Bangla idioms, along with a synthetic multiple‑choice question set for idiom meaning identification. It evaluates recent large language models on three idiom‑related tasks—paraphrasing, idiom span detection, and meaning identification—using zero‑shot and few‑shot prompting. Results show significant variability across models, with Phi‑4‑mini‑instruct best at paraphrasing, Kimi‑K2‑32b‑instruct excelling at span detection, and Gemini‑2.5‑flash leading in meaning identification.

By Mousumi Akter, Md. Faiyaz Abdullah Sayeedi, Nurul Labib Sayeedi, Swakkhar Shatabda