Papers with CAR
Defending Against Disinformation Attacks in Open-Domain Question Answering (2024.eacl-short)
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| Challenge: | Existing methods to defend against data poisoning attacks in open-domain question answering are lacking . |
| Approach: | They propose a method that uses query augmentation to find diverse passages that could answer the original question but are less likely to have been poisoned. |
| Outcome: | The proposed method provides gains of nearly 20% exact match across varying levels of data poisoning/knowledge conflicts. |
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)
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Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu
| Challenge: | Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text. |
| Approach: | They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes. |
| Outcome: | The proposed benchmark improves by 4.1% over baselines on SCITAT. |
Stronger Models are Not Always Stronger Teachers for Instruction Tuning (2025.naacl-long)
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| Challenge: | Existing methods to optimize instruction-following capabilities of large language models (LLMs) assume that larger or stronger models are stronger teachers and therefore adopt smaller models as response generators. |
| Approach: | They propose to use large-scale instruction datasets to tune large language models to align with specific tasks and user intents. |
| Outcome: | The proposed metric outperforms most baselines in identifying the effectiveness of response generators. |
CAR: Empowering Agents with Dynamic Tool Synthesis and Global Trajectory Rectification (2026.findings-acl)
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| Challenge: | Existing LLM agents are brittle in open-ended environments due to two limitations: 1) a closed action space; 2) myopic error recovery. |
| Approach: | They propose a novel architecture that augments the action space and revises global strategies by adding a reflective replanning mechanism to the system. |
| Outcome: | Experiments show that CAR outperforms baselines in a diagnostic benchmark with pruned toolsets to simulate tool scarcity. |
CAR: Conceptualization-Augmented Reasoner for Zero-Shot Commonsense Question Answering (2023.findings-emnlp)
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| Challenge: | Existing approaches to zero-shot commonsense question answering use incomplete CSKBs . lack of human annotations makes sampled negative examples potentially uninformative and contradictory. |
| Approach: | They propose a framework that abstracts a commonsense knowledge triple to many higher-level instances, which increases the coverage of the CSKB and expands the ground-truth answer space. |
| Outcome: | Experiments show that CAR can generalize to zero-shot commonsense scenarios . lack of human annotations makes sampled negative examples potentially uninformative and contradictory. |