Papers with CDT
CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities. |
| Approach: | They propose a Cognition-Domain-Task framework which measures a model’s capabilities across three dimensions. |
| Outcome: | The proposed framework improves performance on dataset evaluation and data selection, while achieving higher scores on general and specific benchmarks. |
CDS: Data Synthesis Method Guided by Cognitive Diagnosis Theory (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but the complexity of emerging tasks and higher performance demands highlight the need for continuous improvement. |
| Approach: | They propose a method that refines evaluation results and characterizes model profiles at the knowledge component level. |
| Outcome: | The proposed method improves performance across multiple benchmarks and academic exams. |
Deriving Character Logic from Storyline as Codified Decision Trees (2026.acl-long)
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| Challenge: | Existing behavioral profiles are unstructured, weakly validated, and unusable . existing models are weakly valid, leading to brittle agent behavior . Using codified decision trees, we show that CDT outperforms previous methods . |
| Approach: | They propose a data-driven framework that induces an executable decision structure from narrative data. |
| Outcome: | The proposed framework outperforms human-written profiles and prior profiles on multiple benchmarks. |
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (D19-1)
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| Challenge: | Existing methods to identify sentiment polarity of opinion words are cumbersome due to the amount of opinionated material on the internet. |
| Approach: | They propose a method to identify sentiment polarity of opinion words on a specific aspect of a sentence using neural networks. |
| Outcome: | The proposed method is the state-of-the-art in aspect-based sentiment classification. |