arXiv Machine Learning

IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems

arXiv:2607. 23322v1 Announce Type: cross Abstract: Instruction tuning has become the standard method for adapting large language models to follow human intent, yet existing instruction datasets are dominated by English-language general-knowledge tasks and lack coverage of specialized pedagogical domains.

arXiv Machine Learning

1d ago

L3Cube-IndicQuest v2: A Large-Scale Multilingual Benchmark for Evaluating Factual Knowledge of Large Language Models Across Indic Languages

arXiv:2608. 15535v1 Announce Type: cross Abstract: We present L3Cube-IndicQuest v2, a large-scale gold-standard multilingual question-answering benchmark for evaluating the India-specific factual knowledge of Large Language Models (LLMs).

By Rinit Jain, Tirthraj Mahajan, Advait Joshi, Raviraj Joshi

llms benchmarks

arXiv Machine Learning

Jun 8

Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios

arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.

By Tao Liu, Ye Lu, Ruohua Zhang, Siyu Song, Wentao Liu, Aimin Zhou, Hao Hao

llms agents benchmarks safety

arXiv Machine Learning

Jul 28

BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis

arXiv:2607. 23319v1 Announce Type: cross Abstract: Standard subword tokenization algorithms such as Byte-Pair Encoding (BPE) and SentencePiece are trained predominantly on modern language corpora and produce inefficient segmentations when applied to classical Indian languages.

By Poornima Kumaresan, Pavithra Muruganantham, Lakshmi Rajendran, Santhosh Sivasubramani

llms nlp benchmarks

arXiv AI

Aug 11

ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models

arXiv:2608. 09548v1 Announce Type: cross Abstract: Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators.

By Yilin Jiang, Xiaorong Zhu, Fei Tan, Zicheng Zhang, Kaiyi Huang, Yang Yu, Zexuan Fei, Yiming Luo, Keqian Li, Hao Hao, Aimin Zhou, Guangtao Zhai

llms nlp benchmarks safety

arXiv AI

Aug 5

TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring

arXiv:2608. 03952v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to provide conversational practice for English-as-a-second-language (ESL) learners.

By Dongjie Yang, Siyan Lin, Leixian Shen, Rui Sheng, Huamin Qu, Zixin Chen

llms fine-tuning benchmarks

arXiv Machine Learning

Jul 28

No Optimal Language Set Exists for Multilingual Instruction Tuning: Insights from a Linguistically-Informed Study

arXiv:2410. 07809v2 Announce Type: replace-cross Abstract: Multilingual instruction tuning (MIT) is challenged by the curse of multilinguality, data scarcity, and high computational cost.

By G\"urkan Soykan, G\"ozde G\"ul \c{S}ahin

fine-tuning benchmarks

arXiv Machine Learning

1d ago

Interpretable Cross-Lingual Alignment in Small Language Models: Probing Cultural and Pragmatic Reasoning in Japanese-English Bilingual LLMs

arXiv:2608. 14896v1 Announce Type: cross Abstract: Large language models work well on English and behave in poorly understood ways on languages typologically far from it.

By Florian Braun

llms benchmarks safety

arXiv AI

1d ago

LLMs Get Smarter from Targeted Synthetic Multilingual Data

arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.

By Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen, Neha Gupta, Andreas Stolcke

llms fine-tuning

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

Jul 29

Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbook

arXiv:2509. 16780v3 Announce Type: replace-cross Abstract: Large language models (LLMs) show promise as educational aids but often lack alignment with specific course materials.

By Eason Chen, Chuangji Li, Eric Li, Zimo Xiao, Jionghao Lin, Kenneth R. Koedinger

llms rag nlp safety

arXiv AI

Jun 26

NebulaExp-8B: An Empirical Post-Training Pipeline via Full-Scale Ablation Research

arXiv:2606. 26671v1 Announce Type: new Abstract: Post-training alignment determines the reasoning and human preference following capabilities of large language models, yet most existing works withhold detailed data construction, filtering rules and training recipes, which hinders community reproducibility and lightweight model optimization.

By Qiaobo Hao, Yangqian Wu, Shunyi Wang, Zhongjian Zhang, Ziqun Li, Yayin He, Muqing Li, Chen Zhong

llms reinforcement-learning fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Jul 24

GLAN-QnA-KR: A Seedless Taxonomy-Driven Korean Instruction Corpus

arXiv:2607. 20443v1 Announce Type: cross Abstract: We release GLAN-QnA-KR, a 303,581-row openly redistributable Korean instruction-QA corpus produced via the seedless taxonomy-driven GLAN synthesis pipeline with Microsoft's Phi-3.

By Daekeun Kim

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