Papers with ASTRA
Self-Training with Weak Supervision (2021.naacl-main)
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| Challenge: | State-of-the-art deep neural networks require large amounts of labeled training data that is expensive to obtain or not available for many tasks. |
| Approach: | They propose a weak supervision framework that leverages all available data for a given task . they leverage task-specific unlabeled data through self-training with a model that predicts pseudo-labels for instances that may not be covered by weak rules . |
| Outcome: | The proposed framework improves on state-of-the-art datasets on six benchmark tasks. |
ASTRA: A Negotiation Agent with Adaptive and Strategic Reasoning via Tool-integrated Action for Dynamic Offer Optimization (2025.emnlp-main)
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| Challenge: | Existing agents struggle due to bounded rationality in human data, low adaptability to counterpart behavior, and limited strategic reasoning. |
| Approach: | They propose a framework for turn-level offer optimization based on two core principles: opponent modeling and Tit-for-Tat reciprocity. |
| Outcome: | The proposed framework outperforms baselines across diverse partner agents and validates through human evaluation. |
ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering (2026.acl-long)
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| Challenge: | Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility . Existing tree-based approaches suffer from limited semantic adaptability . |
| Approach: | They propose a method that leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on complex table benchmarks. |
ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs (2026.acl-long)
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| Challenge: | Existing methods lack the capability for continuous learning and self-evolution from interactions, limiting the diversity and adaptability of attack strategies. |
| Approach: | They propose an automated framework capable of discovering, retrieving, and evolving attack strategies. |
| Outcome: | The proposed framework outperforms existing baselines in a black-box setting. |