Sciriff: A resource to enhance language model instruction-following over scientific literature

D Wadden, K Shi, J Morrison, A Naik, S Singh… - arxiv preprint arxiv …, 2024 - arxiv.org
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset
of 137K instruction-following demonstrations for 54 tasks covering five essential scientific …

Meta-reasoning: Semantics-symbol deconstruction for large language models

Y Wang, Z Zhang, P Zhang, B Yang, R Wang - arxiv preprint arxiv …, 2023 - arxiv.org
Neural-symbolic methods have demonstrated efficiency in enhancing the reasoning abilities
of large language models (LLMs). However, existing methods mainly rely on syntactically …

EXCGEC: A Benchmark of Edit-wise Explainable Chinese Grammatical Error Correction

J Ye, S Qin, Y Li, X Cheng, L Qin, HT Zheng… - arxiv preprint arxiv …, 2024 - arxiv.org
Existing studies explore the explainability of Grammatical Error Correction (GEC) in a limited
scenario, where they ignore the interaction between corrections and explanations. To bridge …

SWE-Fixer: Training Open-Source LLMs for Effective and Efficient GitHub Issue Resolution

C **e, B Li, C Gao, H Du, W Lam, D Zou… - arxiv preprint arxiv …, 2025 - arxiv.org
Large Language Models (LLMs) have demonstrated remarkable proficiency across a variety
of complex tasks. One significant application of LLMs is in tackling software engineering …

eC-Tab2Text: Aspect-Based Text Generation from e-Commerce Product Tables

LAG Guanilo, MT Nayeem, C López… - arxiv preprint arxiv …, 2025 - arxiv.org
Large Language Models (LLMs) have demonstrated exceptional versatility across diverse
domains, yet their application in e-commerce remains underexplored due to a lack of …