Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
LLM s Are Few-Shot In-Context Low-Resource Language Learners
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Broad empirical evaluation finds that fine-tuning heuristics for source-language choice in cross-lingual transfer do not hold reliably under in-context learning.
LLaMA 3.1 extracts visual rating scores from Dutch neuroradiology reports with 87-96% balanced accuracy but only 66-80% on numerical counts, with few-shot prompting raising the latter to 81-92%.
citing papers explorer
-
Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
-
When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
Broad empirical evaluation finds that fine-tuning heuristics for source-language choice in cross-lingual transfer do not hold reliably under in-context learning.
-
Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model
LLaMA 3.1 extracts visual rating scores from Dutch neuroradiology reports with 87-96% balanced accuracy but only 66-80% on numerical counts, with few-shot prompting raising the latter to 81-92%.