Papers with trip planning
Speed Without Sacrifice: Fine-Tuning Language Models with Medusa and Knowledge Distillation in Travel Applications (2025.acl-industry)
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Daniel Zagyva, Emmanouil Stergiadis, Laurens Van Der Maas, Aleksandra Dokic, Eran Fainman, Ilya Gusev, Moran Beladev
| Challenge: | Rapid growth of digital applications has intensified the demand for real-time natural language processing (NLP) capabilities. |
| Approach: | They propose a framework that combines Medusa and knowledge distillation to achieve compounded benefits in both model size and inference speed. |
| Outcome: | The proposed framework reduces inference latency by 10-20x while maintaining the student model’s performance quality. |
Automatic Extraction of Language-Specific Biomarkers of Healthy Aging in Icelandic (2024.lrec-main)
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| Challenge: | Multiple studies have shown that individuals suffering from AD exhibit difficulties with word retrieval, produce fewer information units and content words, and use more pronouns than healthy age-matched controls. |
| Approach: | They administered three language tasks to participants aged 60–80 to examine the effects of task type and healthy aging on various automatically extracted part-of-speech features in Icelandic. |
| Outcome: | The results show that task type and healthy aging influence language production in Icelandic. |
Unlocking the Potential of Diffusion Language Models through Template Infilling (2026.acl-long)
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| Challenge: | Existing methods rely on prefix-based prompting, resulting in a lack of stability and a large computational time. |
| Approach: | They propose a conditioning methodology tailored for Diffusion Language Models that distributes structural anchors across the target response, establishing a global template before infilling masked segments. |
| Outcome: | The proposed method improves on mathematical reasoning, code generation, and trip planning benchmarks while maintaining speed and robustness. |
Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data (2026.findings-acl)
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Xuanming Zhang, Shwan Ashrafi, Aziza Mirsaidova, Amir H. Rezaeian, Miguel Ballesteros, Lydia Chilton, Zhou Yu, Dan Roth
| Challenge: | Recent work has explored reasoning efficiency via test-time scaling and early exit strategies. |
| Approach: | They propose an anytime reasoning framework and the Anytime Index to improve model quality . they also propose an inference-time self-improvement method to produce better intermediate solutions . |
| Outcome: | The proposed method improves on NaturalPlan, AIME, and GPQA datasets and improves reasoning quality and efficiency under budget constraints. |