Papers with large-scale language model
Discrete Prompt Optimization via Constrained Generation for Zero-shot Re-ranker (2023.findings-acl)
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| Challenge: | Existing studies suggest re-rankers by fine-tuning pre-trained language models . however, manual search for discrete prompts is expensive and sub-optimal in transferability . |
| Approach: | They propose a discrete prompt optimization method that guides the generated texts toward optimal prompts . they propose to use large-scale language models as a zero-shot re-ranker . |
| Outcome: | The proposed method improves the performance of the re-ranker against baselines and human prompts. |
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence (2022.emnlp-main)
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| Challenge: | Existing work on question answering models relies on retrieved documents for provenance, but recent studies show that models can retain vast amounts of factual knowledge . retrieval-based generation approaches combine parametric knowledge sources with a large number of retrieved evidence documents, achieving state-of-the-art performance on open retrieval datasets. |
| Approach: | They propose to use parametric and parametric knowledge to generate free-form questions from retrieved evidence documents. |
| Outcome: | The proposed model can use parametric and parametric knowledge to generate free-form answers from retrieved evidence documents. |
CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering (2024.lrec-main)
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| Challenge: | Recent advances in artificial intelligence highlight the potential of language models in psychological health support. |
| Approach: | They propose a method to enhance the precision and efficacy of psychological support through large language models. |
| Outcome: | The proposed model generates professional and structured responses in Chinese psychological health Q&A tasks, showcasing its practicality and quality. |
Narrate Dialogues for Better Summarization (2022.findings-emnlp)
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| Challenge: | Recent work on dialogue summarization models focuses on generating concise summaries for multi-party dialogues. |
| Approach: | They propose several ways to convert dialogue into a third-person narrative style . they propose to use narration as a valuable annotation for LLMs . |
| Outcome: | Empirical results show that the proposed approach achieves higher scores on ROUGE and a factual correctness metric. |
Is GPT-3 a Good Data Annotator? (2023.acl-long)
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| Challenge: | Data annotation is the process of labeling data that could be used to train machine learning models. |
| Approach: | They evaluate the performance of a large-scale language model developed by OpenAI . they compare it with traditional methods and analyze its output on a range of tasks . |
| Outcome: | The proposed model has shown impressive performance on a range of NLP tasks. |
Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task (2022.emnlp-main)
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| Challenge: | Existing methods for synthesizing adversarial negative responses are limited by their scalability and cost. |
| Approach: | They propose a method for generating adversarial negative responses using a large-scale language model. |
| Outcome: | The proposed method outperforms other methods on dialogue selection tasks. |
Retrieval-Augmented Modular Prompt Tuning for Low-Resource Data-to-Text Generation (2024.lrec-main)
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| 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. |