Papers with planning task
Document-Level Planning for Text Simplification (2023.eacl-main)
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| Challenge: | Existing work on text simplification is limited to sentence-level inputs . attempts to iteratively apply these approaches fail to preserve discourse structure of document . |
| Approach: | They propose a simplification plan that labels each sentence in the input document while considering both its context and internal structure. |
| Outcome: | The proposed model outperforms baselines on two simplification benchmarks and when used to guide document-level simplification models. |
Unlocking the Planning Capabilities of Large Language Models with Maximum Diversity Fine-tuning (2025.findings-naacl)
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| Challenge: | Existing studies have shown that LLMs struggle to generate valid plans in the automated planning domain due to weak System 2 competencies. |
| Approach: | They propose a method which uses a random sampling method to select diverse and representative data to enhance sample efficiency and the model’s generalization capability. |
| Outcome: | The proposed method outperforms baseline methods across scales and multiple benchmark domains. |
Open Grounded Planning: Challenges and Benchmark Construction (2024.acl-long)
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| Challenge: | Existing work on LLM-based planning uses language generation to produce free-style plans . however, these plans are not grounded in an executable set of actions . |
| Approach: | They propose a new task for open grounded planning that asks the model to generate an executable plan based on a variable action set. |
| Outcome: | The proposed task is open grounded planning, which is based on a set of variables. |
Semformer: Transformer Language Models with Semantic Planning (2024.emnlp-main)
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| Challenge: | Neural language models (LLMs) employ teacher forcing to predict tokens based on preceding ground truth tokens. |
| Approach: | They propose a method for training a Transformer language model that explicitly models the semantic planning of response. |
| Outcome: | The proposed method exhibits near-perfect performance and mitigates shortcut learning. |