5 papers
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…
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…
Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics
Reza Sanayei, Srdjan Vesic, Eduardo Blanco +1
Large Language Models (LLMs) excel at linear reasoning tasks but remain underexplored on non-linear structures such as those found in natural debates, which are best expressed as a…
Fane at SemEval-2025 Task 10: Zero-Shot Entity Framing with Large Language Models
Enfa Fane, Mihai Surdeanu, Eduardo Blanco +1
Understanding how news narratives frame entities is crucial for studying media's impact on societal perceptions of events. In this paper, we evaluate the zero-shot capabilities of…
Memorization in In-Context Learning
Shahriar Golchin, Mihai Surdeanu, Steven Bethard +2
In-context learning (ICL) has proven to be an effective strategy for improving the performance of large language models (LLMs) with no additional training. However, the exact mecha…