3 papers
cs.CL2026
Two-Stage Prompt Optimization for Few-Shot Relation Extraction: From Reasoning-Guided Search to Gradient-Guided Refinement
Aunabil Chakma, Mihai Surdeanu, Eduardo Blanco
Automatic prompt optimization is still underexplored for episodic few-shot relation extraction with smaller language models. We propose a two-stage framework that combines reasonin…
cs.CL2026
Structured Semantic Information Helps Retrieve Better Examples for In-Context Learning Applied to Few-Shot Relation Extraction
Aunabil Chakma, Mihai Surdeanu, Eduardo Blanco
This paper presents several strategies to automatically obtain additional examples for in-context learning, effectively transforming relation extraction from a 1-shot to a few-shot…
cs.CL2026
ChakmaNMT: Machine Translation for a Low-Resource and Endangered Language via Transliteration
Aunabil Chakma, Aditya Chakma, Masum Hasan +3
We present the first systematic study of machine translation for Chakma, an endangered and extremely low-resource Indo-Aryan language, with the goal of supporting language access a…