Papers with CORE
CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures (2026.eacl-long)
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| Challenge: | Game-theoretic interactions between agents with large language models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been quantified. |
| Approach: | They propose a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions. |
| Outcome: | The proposed metric measures the effectiveness of language use within multi-agent systems across game-theoretic interactions. |
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation (2022.findings-emnlp)
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| Challenge: | Prior work on counterfactual data augmentation only considered restricted classes of perturbations, limiting their effectiveness. |
| Approach: | They propose a retrieval-augmented framework for creating diverse counterfactual perturbations for CDA. |
| Outcome: | Experiments on natural language inference and sentiment analysis show that the proposed framework can be used to encourage diversity in manually authored perturbations. |
CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation. (2023.emnlp-main)
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| Challenge: | Existing datasets that focus on company relations and business entities are lacking in relation classification. |
| Approach: | They introduce a few-shot relation classification dataset for company relations and business entities . they use a dataset that includes 4,708 instances of 12 relation types . |
| Outcome: | The proposed dataset includes 4,708 instances of 12 relation types with corresponding textual evidence extracted from company Wikipedia pages. |
Language Acquisition Device in Large Language Models (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) are less data-efficient than humans, and pre-pretraining on synthetic languages has been proposed to close this gap. |
| Approach: | They propose to pre-pretrain on MP-STRUCT, a formal language whose strings encode hierarchical composition, feature-based dependencies, and long-distance displacement via MERGE, AGREE, and MOVE. |
| Outcome: | The proposed model outperforms k-Shuffle Dyck despite not being definable in C-RASP despite being hierarchically expressive and circuit-theoretically learnable . |