Papers with generation approaches
PoMo: Generating Entity-Specific Post-Modifiers in Context (N19-1)
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Jun Seok Kang, Robert Logan, Zewei Chu, Yang Chen, Dheeru Dua, Kevin Gimpel, Sameer Singh, Niranjan Balasubramanian
| Challenge: | Using crowdsourcing, we show that contextual relevance is necessary for accurate post-modifier generation. |
| Approach: | They introduce entity post-modifier generation as an instance of a collaborative writing task . they build a post- modifier dataset from news articles that provides contextually relevant information about the target entity. |
| Outcome: | The proposed system can generate a post-modifier phrase that provides contextually relevant information about the target entity. |
Beyond Superficial Tests: Adversarial Refinement for Reliable Property-Based Testing (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable proficiency in code generation, yet their application to Property-Based Testing (PBT) remains fraught with a superficiality gap. |
| Approach: | They propose an agentic framework that hardens software properties through Adversarial Refinement. |
| Outcome: | a new framework hardens software properties through Adversarial Refinement that detects and fixes bugs in top-tier libraries. |
SNaRe: Domain-aware Data Generation for Low-Resource Event Detection (2025.emnlp-main)
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| Challenge: | Existing methods for ED struggle with label noise and domain drift when applied to specialized domains. |
| Approach: | They propose a domain-aware synthetic data generation framework composed of three components: Scout, Narrator, and Refiner. |
| Outcome: | The proposed framework outperforms baseline approaches on three diverse domain ED datasets and achieves average F1 gains of 3-7% in the zero-shot/few-shot settings and 4-20% improvement for multilingual generation. |