Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation (2024.lrec-main)
Copied to clipboard
| Challenge: | Data-to-text generation methods are often limited by data sparsity and lack of training data. |
| Approach: | They propose a retrieval-augmented modular prompt tuning method that generates texts with few hallucinations from structured data inputs. |
| Outcome: | The proposed method generates texts with few hallucinations and achieves state-of-the-art performance on a dataset for drone handover message generation. |
Similar Papers
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks (2022.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to build labeled training data from domain-specific data are expensive to obtain. |
| Approach: | They propose a Prompt-based Data Augmentation model which only trains small-scale Soft Promptes in frozen Pre-trained Language Models. |
| Outcome: | The proposed model outperforms several baseline models on four benchmarks and is complementary with unlabeled in-domain data. |
The Power of Scale for Parameter-Efficient Prompt Tuning (2021.emnlp-main)
Copied to clipboard
| Challenge: | Unlike discrete text prompts used by GPT-3, soft prompts are learned through backpropagation and can be tuned to incorporate signals from any number of labeled examples. |
| Approach: | They propose a mechanism for learning "soft prompts" to condition frozen language models to perform specific downstream tasks. |
| Outcome: | The proposed method outperforms fewshot learning using GPT-3 and matches the quality of model tuning as models exceed billions of parameters. |
DPTDR: Deep Prompt Tuning for Dense Passage Retrieval (2022.coling-1)
Copied to clipboard
| Challenge: | Recent studies show that prompt tuning is unfriendly for industrial deployment in dense retrieval tasks. |
| Approach: | They propose to apply prompt tuning to dense retrieval tasks to reduce deployment cost . they propose to use retrieval-oriented intermediate pretraining and unified negative mining . |
| Outcome: | The proposed method outperforms state-of-the-art models on MS-MARCO and Natural Questions. |
The Power of Prompt Tuning for Low-Resource Semantic Parsing (2022.acl-short)
Copied to clipboard
| Challenge: | Prompt tuning is an effective method for adapting pre-trained language models to downstream tasks. |
| Approach: | They propose to use prompt tuning for semantic parsing to map natural language utterances onto formal meaning representations. |
| Outcome: | The proposed method outperforms the fine-tuned model on low-resource splits of Overnight and TOPv2 on language representations with increasing model scale and target representations. |
Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
Copied to clipboard
| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
| Approach: | They evaluate the effectiveness of cycle training by using two models which are inverses of each other to generate text from structured data and one which generates the structured data from natural language text. |
| Outcome: | The proposed approach achieves nearly the same performance as fully supervised approaches on the WebNLG, E2E, WTQ, and WSQL datasets. |
Discourse-Aware Soft Prompting for Text Generation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Recent advances in pre-trained langauge models (PLMs) have made great impact on text generation research. |
| Approach: | They propose to use hierarchical blocking to simulate a higher-level discourse structure of human written text and attention sparsity to learn sparse transformations on the softmax-function. |
| Outcome: | The proposed methods perform better on some generation tasks but don't generalize across all generation tasks. |
Learning to Transfer Prompts for Text Generation (2022.naacl-main)
Copied to clipboard
| Challenge: | Pretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. |
| Approach: | They propose a prompt-based method that learns source prompts and transfers them as target prompts to perform target generation tasks. |
| Outcome: | The proposed method can be used to perform text generation tasks in a transferable setting. |
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL (2024.acl-long)
Copied to clipboard
Yunseon Choi, Sangmin Bae, Seonghyun Ban, Minchan Jeong, Chuheng Zhang, Lei Song, Li Zhao, Jiang Bian, Kee-Eung Kim
| Challenge: | Prompt tuning is an important technique for directing model behaviors and eliciting desired responses. |
| Approach: | They propose to find optimal prompt tokens using soft Q-learning to optimize models for prompt tuning. |
| Outcome: | The proposed method improves on baseline prompt tuning, and the results are more natural and interpretable. |
Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models exhibit strong reasoning when guided by chain-of-thought exemplars . collecting large, high-quality reasoning datasets remains laborious and resource-intensive . |
| Approach: | They propose a prompt-space data augmentation framework for enhancing LLM reasoning . they use a pool of 90 randomly selected reasoning instances to elicit diverse reasoning trajectories . |
| Outcome: | The proposed framework improves accuracy over small-data benchmarks and generalization on out-of-domain reasoning evaluations. |
AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing RAG attacks rely on manipulating user queries, but exploit instructional prompts to manipulate RAG outputs covertly. |
| Approach: | They propose an attack that exploits adversarial instructional prompts to manipulate RAG outputs . they propose a query generation strategy that simulates realistic linguistic variation in user queries . |
| Outcome: | The proposed attack exploits instructional prompts to manipulate RAG outputs . it achieves up to 95.23% attack success rate while maintaining benign functionality . |