Papers with LLM-based generation
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models (2024.findings-acl)
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| Challenge: | Contemporary approaches to generate tabular data are limited due to the lack of external knowledge. |
| Approach: | They propose to use proximal policy optimization to apply GANs and fine-tune Large Language Models to enhance the probability distribution of tabular features. |
| Outcome: | The proposed method improves accuracy of GANs and LLMs over state-of-the-art over three real-world datasets. |
Index-Time Prefix Injection for Multi-Tenant Retrieval: Improving Search Relevance Without Model Fine-Tuning (2026.acl-industry)
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| Challenge: | a single multilingual biencoder handles all retrieval, but these are task-generic and domain-agnostic. |
| Approach: | They propose a training-free method that prepending domain-descriptive prefixes to documents during indexing. |
| Outcome: | The proposed method improves retrieval relevance by prepending natural-language prefixes to documents during indexing. |
Learning to See through Sound: From VggCaps to Multi2Cap for Richer Automated Audio Captioning (2025.emnlp-main)
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| Challenge: | Existing AAC datasets suffer from short and simplistic captions, limiting expressiveness and semantic depth. |
| Approach: | They propose a multi-modal dataset that pairs audio with corresponding video and leverages large language models to generate rich, descriptive captions. |
| Outcome: | The proposed framework outperforms existing benchmarks in caption length, lexical diversity, and human-rated quality. |
ViLegalLM: Language Models for Vietnamese Legal Text (2026.findings-acl)
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| Challenge: | **ViLegalLM** is the first suite of Vietnamese pretrained language models for legal text understanding and generation. |
| Approach: | They propose a suite of Vietnamese pretrained language models for legal text understanding and generation. |
| Outcome: | The proposed models outperform instruction-tuned adaptation on four main Vietnamese legal downstream tasks. |
Towards Trustworthy Smart Contract Synthesis: A Multi-Agent Framework with Lean-Based Verification (2026.acl-long)
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| Challenge: | Smart Contracts are the foundation of Decentralized Finance (DeFi), executing financial logic without trusted intermediaries. |
| Approach: | They propose a framework that integrates LLM-based generation with Lean-based auto-formalization and verification. |
| Outcome: | LeVer is the first trustworthy smart contract synthesis framework that integrates LLM-based generation with Lean-based auto-formalization and verification. |