Summary Neural Retrieval for Knowledge-Grounded Dialogue aclanthology.org
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This article explores the effectiveness of retrieval augmentation in dialogue models for reducing knowledge hallucination and improving knowledge utilization, highlighting the importance of the retrieval component and identifying optimal architectures and decoding strategies for better model performance.
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Key Points
- Retrieval augmentation reduces factual incorrectness and knowledge hallucination in dialogue models.
- Retrieval-augmented architectures consist of retrievers, rankers, and encoder-decoders.
- Retrieval augmentation improves knowledge utilization and conversational ability in dialogue models.
- Poly-encoder Transformers, end-to-end-trained retrievers, and dialogue turn-based retrieval enhance performance.
- Retrieval augmentation demonstrates effectiveness in reducing knowledge hallucination and addressing challenges in open-domain knowledge-grounded dialogue.
Summaries
226 word summary
This article examines retrieval augmentation in dialogue models for addressing factual incorrectness and knowledge hallucination. It demonstrates the effectiveness of retrieval augmentation in improving knowledge utilization and reducing hallucination. The study analyzes different components of retrieval augmentation architectures and proposes retrieval-augmented generation (RAG) as a solution. The study highlights the importance of the retrieval component for performance and identifies the DPR-Poly setup and FiD-RAG model as achieving the best retrieval metrics. Models that condition on multiple documents simultaneously show higher engagement and consistency scores. The FiD architecture is optimal for considering multiple documents jointly. The study emphasizes the need for metrics like Knowledge F1 to capture hallucination within models and emphasizes the effectiveness of retrieval-augmented generation in reducing knowledge hallucination while maintaining conversational ability. The authors also explore the impact of different encoder-decoder architectures, sources of knowledge, number of retrieved and re-ranked documents, and decoding strategies on model performance. They find that larger models have lower perplexity but may sacrifice knowledge use for fluency. Limiting the knowledge base to topics present in the dataset improves performance, while scaling up the knowledge source reduces performance. Increasing the number of retrieved documents improves perplexity and F1 measures but leads to higher levels of hallucination. Beam search with beam-blocking yields the highest F1 scores, while nucleus sampling with a higher p value increases variety but results in more hallucination.
279 word summary
This article explores the use of retrieval augmentation in dialogue models to address factual incorrectness and knowledge hallucination. The models utilize retrievers, rankers, and encoder-decoders to generate coherent responses based on multi-turn dialogue context. Experimental results on the Wizard of Wikipedia and CMU Document Grounded Conversations datasets demonstrate the effectiveness of retrieval augmentation in improving knowledge utilization and reducing hallucination. The study analyzes different components of retrieval augmentation architectures, such as Poly-encoder Transformers and end-to-end-trained retrievers, to assess their impact on performance. The authors propose retrieval-augmented generation (RAG) as a solution, comparing different architectures and retrieval methods on the two datasets. The study highlights the importance of the retrieval component for performance, with the DPR-Poly setup and FiD-RAG model achieving the best retrieval metrics. Models that condition on multiple documents simultaneously show higher engagement and consistency scores. The FiD architecture is optimal for considering multiple documents jointly. The study emphasizes the need for metrics like Knowledge F1 to capture hallucination within models and emphasizes the effectiveness of retrieval-augmented generation in reducing knowledge hallucination while maintaining conversational ability. The authors also explore the impact of different encoder-decoder architectures, sources of knowledge, number of retrieved and re-ranked documents, and decoding strategies on model performance. They find that larger models have lower perplexity but may sacrifice knowledge use for fluency. Limiting the knowledge base to topics present in the dataset improves performance, while scaling up the knowledge source reduces performance. Increasing the number of retrieved documents improves perplexity and F1 measures but leads to higher levels of hallucination. Beam search with beam-blocking yields the highest F1 scores, while nucleus sampling with a higher p value increases variety but results in more hallucination.
373 word summary
This article discusses the use of retrieval augmentation in dialogue models to reduce factual incorrectness and knowledge hallucination. The models incorporate retrievers, rankers, and encoder-decoders to generate conversationally coherent responses based on multi-turn dialogue context. The effectiveness of retrieval augmentation is demonstrated through experiments on the Wizard of Wikipedia and CMU Document Grounded Conversations datasets, showing improved knowledge utilization and reduction in hallucination. Different components of retrieval augmentation architectures, such as Poly-encoder Transformers and end-to-end-trained retrievers, are analyzed for their impact on performance. The study concludes that retrieval augmentation significantly enhances the performance of dialogue models in open-domain knowledge-grounded dialogue.
The authors propose retrieval-augmented generation (RAG) as a solution to reduce knowledge hallucination and improve conversational ability in conversational agents. They compare different architectures and retrieval methods for RAG on the Wizard of Wikipedia and CMU-DoG datasets. The strength of the retrieval component is found to be crucial for performance, with the DPR-Poly setup and FiD-RAG model achieving the best retrieval metrics and demonstrating the importance of retrieval for knowledge-grounded dialogue.
Models that condition on multiple documents simultaneously result in more engaging conversationalists, with RAG-Token, RAG-Turn, and FiD-RAG achieving higher F1 scores and better engagement/consistency scores compared to RAG-Sequence. The FiD architecture is optimal for considering multiple documents jointly, with higher F1/KF1 scores and lower human-evaluated hallucination. However, all models suffer from more hallucination when conditioned on more documents.
The study highlights the need for metrics like Knowledge F1 to capture hallucination within models and emphasizes the effectiveness of retrieval-augmented generation in reducing knowledge hallucination while maintaining conversational ability and generalization to unseen distributions.
The authors also explore the impact of different encoder-decoder architectures, sources of knowledge, number of retrieved and re-ranked documents, and decoding strategies on model performance. They find that larger models tend to have lower perplexity but may sacrifice knowledge use for conversational fluency. Limiting the knowledge base to topics present in the dataset improves performance, while scaling up the knowledge source reduces performance. Increasing the number of retrieved documents improves perplexity and F1 measures but leads to higher levels of hallucination. Beam search with beam-blocking yields the highest F1 scores, while nucleus sampling with a higher p value increases variety but results in more hallucination. Pre-training the neural retriever
824 word summary
Retrieval augmentation is a technique that reduces factual incorrectness and knowledge hallucination in state-of-the-art dialogue models. By incorporating neural retrieval in the loop, these models are able to generate conversationally coherent responses based on complex multi-turn dialogue context. The retrieval-augmented architectures consist of retrievers, rankers, and encoder-decoders, with the goal of maximizing knowledgeability while retaining conversational ability. These models have been shown to achieve state-of-the-art performance on knowledge-grounded conversational tasks, demonstrating open-domain conversational capabilities and effective generalization to scenarios not seen during training. Human evaluations have confirmed that these models substantially reduce knowledge hallucination.
The effectiveness of retrieval augmentation is demonstrated in experiments using the Wizard of Wikipedia (WoW) and CMU Document Grounded Conversations (CMU-DoG) datasets. Retrieval-augmented models outperform baselines without retrieval augmentation, with significant improvements in knowledge utilization as measured by Knowledge F1 scores. Human evaluations further confirm the reduction of hallucination in model responses.
Different components of retrieval augmentation architectures are analyzed to determine their impact on performance. Improvements are made by employing Poly-encoder Transformers for finer-grained context-candidate scoring, utilizing end-to-end-trained retrievers in the Fusion-in-Decoder technique, and implementing a dialogue turn-based retrieval mechanism to better incorporate contextual information. These enhancements result in state-of-the-art performance on knowledge-grounded conversational tasks.
The ablations and comparisons conducted in the experiments reveal that retrieval augmentation significantly enhances the performance of dialogue models. By incorporating retrievers trained through retrieval-augmented generation, models achieve better knowledge utilization, reduce hallucination, and improve overall conversational ability. The use of neural retrieval in open-domain knowledge-grounded dialogue proves to be an effective approach for addressing the challenges posed by this task.
In this work, the authors focus on the problem of knowledge hallucination in conversational agents. They propose retrieval-augmented generation (RAG) as a solution to reduce hallucination and improve conversational ability. They compare different architectures and retrieval methods for RAG on the Wizard of Wikipedia (WoW) and CMU-DoG datasets.
The authors find that the strength of the retrieval component is crucial for performance. The DPR-Poly setup, which combines dense passage retrieval (DPR) with a poly-encoder re-ranker, obtains the best retrieval metrics on WoW. The FiD-RAG model, which uses a retriever tuned for open-domain dialogue, demonstrates the importance of retrieval for knowledge-grounded dialogue.
Models that condition on multiple documents simultaneously result in more engaging conversationalists. RAG-Token, RAG-Turn, and FiD-RAG achieve higher F1 scores and better engagement/consistency scores compared to RAG-Sequence. RAG-Turn also shows benefits for automated metrics when conditioning on turns of dialogue independently.
The FiD architecture is optimal when considering multiple documents jointly, with higher F1/KF1 scores and lower human-evaluated hallucination. However, all models suffer from more hallucination when conditioned on more documents.
Standard metrics used for open-domain dialogue are not sufficient to capture hallucination within models. Metrics like Knowledge F1 are necessary to study model performance.
Overall, the authors demonstrate that retrieval-augmented generation can reduce knowledge hallucination in conversational agents while maintaining conversational ability and generalizing to unseen distributions.
References: - Adiwardana, D., Luong, M. T., So, R., Hall, J., Fiedel, N., Thoppilan, R., ... & Amodei, D. (2020). Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977. - Roller, S., Dinan, E., Goyal, N., Ju, D., Williamson, M., Liu, J., ... & Weston, J. (2021). Recipes for building an open-domain chatbot. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume (pp. 300-325). - Lewis, P. S., Perez, E., Pikthus, A., Petroni, F., Karpukhin, V., Goyal, N.,
This article discusses various aspects of neural retrieval for knowledge-grounded dialogue. The authors compare different models and architectures, evaluate the impact of pre-training, decoding strategies, and the source of knowledge on model performance.
In terms of model comparison, the authors find that different encoder-decoder architectures yield similar results in terms of generation metrics. However, larger models tend to have lower perplexity but may sacrifice knowledge use for conversational fluency. The inclusion of a neural model trained for retrieval is found to be necessary for improved performance.
The authors also explore the effects of different sources of knowledge on model performance. They find that limiting the knowledge base to topics present in the dataset improves performance, while scaling up the knowledge source to the full Wikipedia knowledge base reduces performance.
The number of retrieved and re-ranked documents is found to affect model performance. Increasing the number of retrieved documents improves perplexity and F1 measures, but leads to higher levels of hallucination. The authors suggest that limiting the number of re-ranked documents to 25 strikes a balance between performance and computational overhead.
The decoding strategy used also impacts model performance. Beam search with beam-blocking yields the highest F1 scores, while nucleus sampling with a higher p value increases variety but results in more hallucination.
Pre-training the neural retriever does not yield noticeable downstream gains, suggesting that fine-tuning is sufficient. Similarly, pre-training the re-ranker does not substantially improve performance.
Overall, the study highlights the importance of neural retrieval for knowledge-grounded dialogue and provides insights into various factors that affect model performance.