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cs.CL2026
TaxoSAE: Rethinking Sparse Autoencoders through Taxonomic Logic
Xinyu Guo, Minglai Yang, Clayton T. Morrison +2
Recent Sparse Autoencoders (SAEs) decompose the representations of large language models (LLMs) into interpretable features, enabling precise readout and control of encoded knowled…
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…