Papers with ART
ART: Adaptive Reasoning Trees for Explainable Claim Verification (2026.findings-eacl)
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Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, Abbaas Alif Mohamed Nishar, Pranab Mohanty, Swapnil Shinde, Yue Wu
| Challenge: | Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. |
| Approach: | They propose a hierarchical method for claim verification that uses a root claim and a pairwise tournament of its children to determine an argument's strength. |
| Outcome: | The proposed method outperforms baseline methods on multiple datasets and shows that it is more reliable and clearer than existing methods. |
Adaptive Reinforcement Tuning Language Models as Hard Data Generators for Sentence Representation (2024.lrec-main)
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| Challenge: | Existing methods use contrastive learning (CL) to learn effective sentence representations, but require extensive human annotation. |
| Approach: | They propose a reinforcement learning approach for fine-tuning small-parameter LLMs to generate high-quality hard contrastive data without human feedback. |
| Outcome: | The proposed method achieves state-of-the-art on seven semantic text similarity tasks. |
Questions Are All You Need to Train a Dense Passage Retriever (2023.tacl-1)
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| Challenge: | Existing methods for dense retrieval require large supervised datasets with custom hard-negative mining and denoising of positive examples. |
| Approach: | They propose a new corpus-level autoencoding approach for training dense retrieval models that does not require labeled training data. |
| Outcome: | The proposed method matches or surpasses strong supervised performance levels on multiple QA benchmarks with no labeled training data or task-specific losses. |
A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models (2026.eacl-long)
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Iwona Christop, Mateusz Czyżnikiewicz, Paweł Skórzewski, Łukasz Bondaruk, Jakub Kubiak, Marcin Lewandowski, Marek Kubis
| Challenge: | Existing benchmarks for testing audio modality of multimodal large language models focus on testing audio tasks in isolation. |
| Approach: | They propose a new benchmark to assess multimodal large language models' ability to combine audio tasks. |
| Outcome: | The proposed benchmarks show that multimodal models can solve problems that require reasoning over audio signals with satisfactory results. |
ART: The Alternating Reading Task Corpus for Speech Entrainment and Imitation (2024.lrec-main)
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Zheng Byron Yuan, Dorina de Jong, Ruitao Feng, Štefan Beňuš, Noël Nguyen, Róbert Sabo, Luciano Fadiga, Alessandro D’Ausilio
| Challenge: | The Alternating Reading Task (ART) Corpus is a collection of dyadic sentence readings for studying the entrainment and imitation behaviour in speech communication. |
| Approach: | They propose to use dyadic sentence reading to study entrainment and imitation in speech communication. |
| Outcome: | The proposed study includes three conditions and three subcorpora encompassing French-, Italian-, and Slovak-accented English. |
Natural Language Reasoning in Large Language Models: Analysis and Evaluation (2025.findings-acl)
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| Challenge: | Argumentative reasoning presents unique challenges due to its reliance on context, implicit assumptions, and value judgments. |
| Approach: | They propose a large-scale evaluation of LLMs' unconstrained natural language reasoning capabilities . they formalise a new strategy designed to evaluate argumentative reasoning in LLM . |
| Outcome: | The proposed model performs better on a range of reasoning tasks than other models. |
Automated Progressive Red Teaming (2025.coling-main)
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| Challenge: | Automated red teaming (ART) is effective but time-consuming, costly and lacks scalability. |
| Approach: | They propose an automated red teaming framework that generates adversarial prompts to expose LLM vulnerabilities. |
| Outcome: | The proposed framework explores and exploits LLM vulnerabilities through multi-round interactions. |
ART: Attention-Regularized Transformers for Multi-Modal Robustness (2026.findings-eacl)
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| Challenge: | Existing approaches to enhancing robustness are domain-specific or lack formal guarantees. |
| Approach: | They propose a framework that enhances robustness across modalities by regularizing attention maps under adversarial perturbations. |
| Outcome: | The proposed framework improves robustness across modalities and training on IMDB, QNLI, CIFAR-10, Cifar-100, and Imagenette. |
The ART of LLM Refinement: Ask, Refine, and Trust (2024.naacl-long)
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Kumar Shridhar, Koustuv Sinha, Andrew Cohen, Tianlu Wang, Ping Yu, Ramakanth Pasunuru, Mrinmaya Sachan, Jason Weston, Asli Celikyilmaz
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations and self-improve? |
| Approach: | They propose a reasoning with a refinement strategy called *ART: Ask, Refine, and Trust* that asks necessary questions to decide when an LLM should refine its output and uses it to affirm or deny trust. |
| Outcome: | The proposed reasoning with a refinement strategy achieves a performance gain of +5 points over baselines on two multistep reasoning tasks. |
Hint-Based Training for Non-Autoregressive Machine Translation (D19-1)
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| Challenge: | AutoRegressive Translation models have to generate tokens sequentially during decoding and thus suffer from high inference latency. |
| Approach: | They propose to use hidden states and word alignments to help train NART models. |
| Outcome: | The proposed model improves on the WMT14 En-De and De-En datasets but is faster in inference than the current models. |
ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)
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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu
| Challenge: | Existing studies focus on language-based premises and deduce valid conclusions from visual observations. |
| Approach: | They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning . |
| Outcome: | Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts . |
ART: Attention Replacement Technique to Improve Factuality in LLMs (2026.acl-long)
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| Challenge: | Existing methods to mitigate hallucinations in large language models are expensive and require significant resources. |
| Approach: | They propose a training-free method that replaces uniform attention patterns in shallow layers with local attention patterns to reduce hallucinations. |
| Outcome: | The proposed method reduces hallucinations across multiple LLM architectures. |
Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models (2026.acl-long)
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Kai Hu, Abhinav Aggarwal, Mehran Khodabandeh, David Zhang, Eric Hsin, Li Chen, Ankit Jain, Matt Fredrikson, Akash Bharadwaj
| Challenge: | Existing approaches to red teaming are based on example-based evaluation, where a static list of specific prompts is used to define and measure "unsafe content" |
| Approach: | They propose a new automated red teaming framework that shifts from example-based to policy-based evaluation that focuses on risk coverage, semantic diversity, and fidelity. |
| Outcome: | The proposed method achieves superior, human-readable attacks against open-source and proprietary models even for unseen safety policies. |