Papers with ILP
Textual Analogy Parsing: What’s Shared and What’s Compared among Analogous Facts (D18-1)
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| Challenge: | Existing methods to extract information from text do not capture disparity between demographic groups. |
| Approach: | They propose a task of Textual Analogy Parsing to model higher-order meanings by comparing poverty rates between different demographic groups. |
| Outcome: | The proposed model can be used to generate graphs from quantitative text. |
Robustness Evaluation of Text Classification Models Using Mathematical Optimization and Its Application to Adversarial Training (2022.findings-aacl)
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| Challenge: | Neural networks are vulnerable to adversarial examples due to slightly perturbed input data. |
| Approach: | They propose a method that evaluates the robustness of text classification models by an optimization problem that identifies a minimum synonym swap that changes the classification result. |
| Outcome: | The proposed method achieves high scores in human evaluations of grammatical correctness and semantic similarity for an IMDb dataset and implements adversarial training with the IMD and SST2 datasets. |
A Differentiable Integer Linear Programming Solver for Explanation-Based Natural Language Inference (2024.lrec-main)
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| Challenge: | Existing ILP frameworks are non-differentiable and cannot be integrated as part of a broader deep learning architecture. |
| Approach: | They propose a neuro-symbolic architecture for explanation-based NLI based on DBCS. |
| Outcome: | The proposed approach achieves superior performance when compared to existing solvers and black-box solver. |
Diff-Explainer: Differentiable Convex Optimization for Explainable Multi-hop Inference (2022.tacl-1)
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| Challenge: | Existing explainable multi-hop inference models are regarded as black-boxes due to their ability to transfer linguistic and semantic information to downstream tasks, posing concerns about interpretability and transparency of their predictions. |
| Approach: | They propose a hybrid framework that integrates explicit constraints with neural architectures through differentiable convex optimization to answer and explain multi-hop questions in natural language. |
| Outcome: | The proposed framework improves performance on scientific and commonsense QA tasks while still providing structured explanations in support of its predictions. |
Provable Fast Greedy Compressive Summarization with Any Monotone Submodular Function (N18-1)
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| Challenge: | Submodular maximization with the greedy algorithm is an effective approach to extractive summarization. |
| Approach: | They propose a submodular maximization method that is 100 to 400 times faster than existing methods for extractive summarization. |
| Outcome: | The proposed method is 100 to 400 times faster than existing method based on integer-linear-programming formulations and achieves 95%-approximation. |
Learning Transition Patterns by Large Language Models for Sequential Recommendation (2025.coling-main)
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Jianyang Zhai, Zi-Feng Mai, Dongyi Zheng, Chang-Dong Wang, Xiawu Zheng, Hui Li, Feidiao Yang, Yonghong Tian
| Challenge: | Extensive experiments on six real-world datasets show our approach outperforms the best baselines by 7.33% in NDCG@10, 4.65% in Recall@10 and 8.42% in MRR. |
| Approach: | They propose a framework for mapping sequential item texts to sequential item IDs that incorporates multi-query input and item linear projection to model conditional probability distribution of items. |
| Outcome: | The proposed framework outperforms baseline models on six real-world datasets by 7.33% and 4.65% respectively. |
Modeling Document-level Causal Structures for Event Causal Relation Identification (N19-1)
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| Challenge: | a study aims to identify all the event causal relations in a document, both within a sentence and across sentences . main challenges for achieving comprehensive causal relation identification are sparse among all possible event pairs . few causal relations are explicitly stated, especially for identifying cross-sentence causal relations . |
| Approach: | They propose to identify all event causal relations in a document, both within a sentence and across sentences. |
| Outcome: | The proposed model improves the performance of causal relation identification . it shows that the model can be used to identify cross-sentence causal relations . |
Joint Reasoning for Temporal and Causal Relations (P18-1)
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| Challenge: | a cause must occur earlier than its effect, temporal and causal relations are closely related . a joint inference framework is developed for studying temporal, causal relations . |
| Approach: | They propose a joint inference framework for temporal and causal relations . they use constraints inherent in time and causality to enforce constraints . |
| Outcome: | The proposed framework improves extraction of temporal and causal relations from text. |
Easy First Relation Extraction with Information Redundancy (D19-1)
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| Challenge: | Existing relation extraction models make decisions globally using integer linear programming . Existing approaches require time and memory to encode redundant information for ILP . |
| Approach: | They propose an easy first approach for relation extraction with information redundancies embedded in local sentence extractors to resolve conflict decisions with domain and uniqueness constraints. |
| Outcome: | The proposed approach outperforms both ILP and neural network-based methods in relation extraction (RE) studies have shown that the proposed approach improves the efficiency and accuracy of RE models. |
CROP: Contextual Region-Oriented Visual Token Pruning (2025.emnlp-main)
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| Challenge: | Existing VLMs process entire images, leading to excessive visual tokens . redundant image information also introduces a large number of visual token, requiring much higher memory and computation in VLM. |
| Approach: | They propose a framework to prune visual tokens using localization and pruning . they propose CROP to locate local image regions relevant to the query . |
| Outcome: | The proposed framework outperforms existing visual token pruning methods on a wide range of tasks. |
Query-focused Sentence Compression in Linear Time (D19-1)
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| Challenge: | Existing techniques for constrained compression are slow and require third-party solvers. |
| Approach: | They propose a query-focused sentence compression technique which constructs length and lexically constrained compressions in linear time by growing a subgraph in the dependency parse of a sentence. |
| Outcome: | The proposed technique achieves an 11x empirical speedup over baseline methods while improving query-focused applications. |
An Improved Neural Baseline for Temporal Relation Extraction (D19-1)
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| Challenge: | Existing datasets are small and/or have low inter-annotator agreements. |
| Approach: | They propose a new neural system that achieves 10% absolute accuracy improvement over the previous best system. |
| Outcome: | The proposed system achieves 10% absolute improvement over the previous best system on two benchmark datasets. |
Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)
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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim
| Challenge: | Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Approach: | They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns. |
| Outcome: | The proposed framework maps incomplete learning to causes using observable training and inference signals. |
DPLoRA: A Dual-Pruning Framework based on ILP Optimization and Progressive Pruning for Parameter-Efficient LoRA Fine-Tuning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) require computational resources for fine-tuning. |
| Approach: | They propose a framework that optimizes rank allocation via two stages . they propose an initial pruning stage and a progressive pruning stage . |
| Outcome: | The proposed framework outperforms existing PEFT baselines on GLUE and instruction-following tasks while reducing training time and trainable parameters by over 80%. |