Revisiting the Systematicity in Negation in the Era of In-Context Learning

2026-06-15 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

A study on "Revisiting the Systematicity in Negation in the Era of In-Context Learning" investigates Large Language Models' (LLMs) ability to understand negated sentences. Published on 2026-06-15, the research explores both behavioral and representational systematicity. For behavioral aspects, it confirms that LLMs can recognize negation expressions and their scope through in-context learning and demonstrations, though not perfectly. The difficulty of scope recognition is noted to depend on the output format. Regarding representational systematicity, the analysis examines the robustness of constructing "function vectors" from in-context examples for negation tasks. Experiments indicate that while function vectors can be composed for extracting negation cues, their extraction for recognizing negation scope proves more challenging for LLMs.

Key takeaway

For NLP Engineers evaluating LLM performance on complex linguistic tasks, recognize that current models, even with in-context learning, struggle with perfect negation scope recognition. You should carefully design prompts and output formats, as these significantly influence an LLM's ability to correctly interpret negated sentences. Consider specialized fine-tuning or architectural adjustments if your application critically depends on precise negation understanding.

Key insights

LLMs partially grasp negation via in-context learning, but struggle with robust scope recognition and function vector composition for it.

Principles

• LLMs recognize negation cues via ICL.
• Negation scope recognition remains difficult.
• Output format affects negation task performance.

Method

The study analyzes behavioral systematicity by testing LLM negation recognition via demonstrations and in-context learning. Representational systematicity is assessed by constructing function vectors from in-context examples for negation tasks.

Topics

• Large Language Models
• In-Context Learning
• Negation Understanding
• Linguistic Systematicity
• Function Vectors
• NLP Evaluation

Best for: Research Scientist, AI Scientist, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.