Papers with structured inference
Randomized Deep Structured Prediction for Discourse-Level Processing (2021.eacl-main)
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| Challenge: | Expressive text encoders have been at the center of recent NLP work . however, some tasks require complex structural dependencies between texts . |
| Approach: | They propose to leverage deep structured prediction and expressive neural encoders for argumentation mining tasks. |
| Outcome: | The proposed framework can be used for argumentation mining tasks without expensive inference tools. |
Reasoning is All You Need for Video Generalization: A Counterfactual Benchmark with Sub-question Evaluation (2025.findings-acl)
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Qiji Zhou, YiFan Gong, Guangsheng Bao, Hongjie Qiu, Jinqiang Li, Xiangrong Zhu, Huajian Zhang, Yue Zhang
| Challenge: | Existing multimodal benchmarks often overlook counterfactual reasoning, which is crucial for robust video understanding. |
| Approach: | They propose a multidimensional multimodal benchmark that systematically evaluates MLLMs across the abstract-concrete and perception-cognition dimensions. |
| Outcome: | The proposed model decomposes complex queries into structured sub-questions, enabling fine-grained reasoning analysis. |
A Conditional Splitting Framework for Efficient Constituency Parsing (2021.acl-long)
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| Challenge: | Developing efficient and effective parsing solutions has always been a key focus in NLP. |
| Approach: | They propose a generic seq2seq parsing framework that casts constituency parsers into a series of conditional splitting decisions. |
| Outcome: | The proposed framework outperforms state-of-the-art (SoTA) methods in discourse parsing . it is based on a syntactic and discourse parsed model and is linear in number of nodes . |
Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning (2020.emnlp-main)
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| Challenge: | Named entity recognition (NER) is widely adopted in several domains, such as news, medical, and social media. |
| Approach: | They propose a few-shot named entity recognition system based on nearest neighbor learning and structured inference. |
| Outcome: | The proposed method improves F1 scores on standard few-shot NER evaluation tasks by 6% to 16% relative to previous methods. |
Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models increasingly require structured inference, says aaron sagar . meta-learning learns universal constraint propagation policies without task-specific training . standard schedulers are inexpensive but myopic, because they optimize local effects . |
| Approach: | MetaJuLS learns universal constraint propagation policies applicable across languages and tasks without task-specific retraining. |
| Outcome: | MetaJuLS achieves 1.5-2.0 speedups over GPU-optimized baselines while maintaining accuracy within 0.2% of state-of-the-art parsers. |
MolRAG: Unlocking the Power of Large Language Models for Molecular Property Prediction (2025.acl-long)
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| Challenge: | Recent LLMs exhibit limited effectiveness on molecular property prediction task due to semantic gap between representations and natural language and lack of domain-specific knowledge. |
| Approach: | They propose a framework that integrates Chain-of-Thought reasoning for molecular property prediction. |
| Outcome: | The proposed framework outperforms pre-trained LLMs on four datasets and matches supervised methods. |
Path Drift in Large Reasoning Models: How First-Person Commitments Override Safety (2025.emnlp-main)
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| Challenge: | Existing studies on prompt injection and jailbreak attacks primarily target the surface structure of input prompts. |
| Approach: | They propose a three-stage approach to mitigate the risk of Long-CoT reasoning drift . they propose 'path-level defense' strategy that incorporates role attribution correction and metacognitive reflection . |
| Outcome: | The proposed framework reduces refusal rates and ethical evaporation, while ethical escalation and layered disclaimers progressively steer models toward unsafe completions. |