Papers with LLM-based generation

5 papers
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models (2024.findings-acl)

Copied to clipboard

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)

Copied to clipboard

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)

Copied to clipboard

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)

Copied to clipboard

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)

Copied to clipboard

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.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations