Papers with Integer Linear Programming
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. |
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 . |
Using Natural Language Relations between Answer Choices for Machine Comprehension (N19-1)
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| Challenge: | Current approaches to the reading comprehension task quantify the relationship between each question and answer choice independently and pick the highest scoring option. |
| Approach: | They propose a method to leverage natural language relations between answer choices to improve machine comprehension. |
| Outcome: | The proposed model improves the performance of a reading comprehension task by leveraging natural language relations between answer choices. |
SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent studies have shown that very large language models (LLMs) can perform NLP tasks with just in-context learning (ICL) but their utility in other languages is underexplored. |
| Approach: | They propose a novel approach to in-context learning that uses noisy test data to generate more accurate labels for LLMs. |
| Outcome: | Experiments on three tasks and eleven LLMs show that the proposed approach outperforms existing in-context learning baselines on English NLP and reasoning tasks. |
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%. |