The paper reports that fine‑tuned small language models (SLMs) can outperform large frontier models in annotating grammar concepts from learner–tutor transcripts. By training a 0.8B Qwen3.5 model on teacher‑generated data and embedding the annotation rules into adapter weights, the authors deploy an efficient end‑to‑end grammar mastery tracker that matches or exceeds the performance of 4B reference models and GPT‑5.4/5.6 on two human‑curated benchmarks. The lightweight model also cuts serving costs by about 16× and boosts learner engagement and key business metrics in an online experiment.
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.
The paper investigates how trajectory fine‑tuning can enhance small language models (SLMs) as next‑action controllers in retrieval‑augmented question answering. By building a seven‑way action‑prediction task from teacher search traces, the authors fine‑tune SLMs and cross‑lingual SLMs (xSLMs) using LoRA and evaluate on 1,646 held‑out examples, achieving a macro‑F1 of 0.6536 with Granite 4.1 3B. In an end‑to‑end controller/generator swap experiment on 149 trajectories, the fine‑tuned model improves Exact Match from 0.7530 to 0.7946 and token F1 from 0.7783 to 0.8295, demonstrating that trajectory supervision boosts action prediction and evidence‑recording behavior.
The paper introduces Universal Textual Teaching (UTT), a parameter‑update‑free framework that distills knowledge gaps between a Teacher and Student large language model into a reusable natural‑language artifact called a Primer. UTT identifies representative gap cases, then iteratively refines the Primer through a cycle of student attempts, prompter‑generated teaching instructions, teacher demonstrations, and synthesizer consolidation. Experiments on math (Omni‑MATH‑2) and code generation (KernelBench) show significant accuracy gains, outperforming prompt engineering and parameter‑based knowledge distillation, and the Primer generalizes across different Teacher‑Student pairs.
arXiv:2610.09152v1 Announce Type: new Abstract: Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation....
arXiv:2607. 14111v1 Announce Type: cross Abstract: Can small language models detect and report on perturbations their own internal activations?
Bangla-English tutoring requires more than producing a correct translation: learners also need explanations of grammar differences, awareness of their likely errors, and targeted practice. We present...
arXiv:2606. 12117v1 Announce Type: cross Abstract: Benchmark scores often misrepresent a large language model's (LLM's) knowledge, because they rely, e.
arXiv:2608.23493v1 Announce Type: new Abstract: Self-reflection is a powerful mechanism for credit assignment in human learning, converting sparse outcome feedback into actionable guidance. However,...
The paper presents a prompt-based method for minimal-edit grammatical error correction (GEC) that reduces overcorrection in large language models (LLMs). It introduces taxonomy-based instructions, batch prompting to regularize overcorrection, and LLM-assisted prompt optimization, achieving an $F_{0.5}$ score of 78.32 on BEA-2019 with Gemini 3.1-Pro. This approach narrows the performance gap to fine-tuned models while avoiding their infrastructure demands.
arXiv:2609.28470v1 Announce Type: new Abstract: Artificial intelligence offers an unprecedented opportunity to augment human capabilities, yet progress at the frontier has focused primarily on advanc...
The paper presents a study on fine‑tuning open‑source instruction‑tuned language models for a Linear Control Systems course using LoRA. A dataset of 360 system‑user‑assistant conversations was created, and LoRA was applied to Qwen2.5‑3B‑Instruct and Qwen2.5‑7B‑Instruct with ranks r=4, 8, and 16. Evaluation with ROUGE, BERTScore, and structured‑output metrics showed that LoRA improved similarity and stability, with the 7B‑r16 model achieving the highest scores and r=8 offering a good trade‑off between performance and parameter efficiency.
LuxIT is a monolingual instruction‑tuning dataset for Luxembourgish, created by synthesizing instruction‑answer pairs from native texts using the DeepSeek‑R1‑0528 model and a quality‑assurance LLM‑as‑judge process. The resulting 227,507 high‑quality pairs were used to fine‑tune 14 LLMs (≤15 B parameters), yielding an average accuracy increase of +5.37 percentage points on standardized Luxembourgish proficiency exams and improvements in macro‑averaged F1 on nine of the fourteen downstream NLP tasks. These findings demonstrate that synthetic monolingual data can effectively enhance LLM performance in low‑resource languages and reveal the complex relationship between exam performance and practical NLP gains.