# A Survey on Retrieval-Augmented Text Generation

This <mark style="color:blue;">**February 2022**</mark> paper provides a survey on the topic of retrieval-augmented text generation, a technique that *<mark style="color:yellow;">**combines deep learning with traditional retrieval methods**</mark>* to increase the performance and utility of large language model applications.

The approach has demonstrated superior performance by *<mark style="color:yellow;">**leveraging existing human-written texts or other external knowledge sources**</mark>* to guide the generation process, enhancing both the quality and relevance of the generated content.

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A Survey on Retrieval-Augmented Text Generation
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### <mark style="color:purple;">Formulation and key components</mark>

<mark style="color:green;">**Formulation**</mark>

Retrieval-augmented text generation is described as an approach where the model, besides the usual input sequence (x), also leverages an additional set of relevant instances (z) retrieved from training sets or external data sources.&#x20;

This extra layer of information (z) aims to enrich the model's output (y), enhancing the generation process's relevance and accuracy.

<mark style="color:green;">**Retrieval Sources**</mark>

* <mark style="color:blue;">**Training Corpus**</mark><mark style="color:blue;">:</mark> The model retrieves relevant examples from its training data, using these instances as references to guide the generation process and reduce uncertainty.
* <mark style="color:blue;">**External Data**</mark><mark style="color:blue;">:</mark> Using external datasets provides additional, potentially uncontained information in the training set, aiding in scenarios like domain adaptation or updating the model's knowledge base.
* <mark style="color:blue;">**Unsupervised Data**</mark><mark style="color:blue;">:</mark> Particularly in machine translation, the approach involves retrieving target language sentences directly from unsupervised (monolingual) corpora, aligning source and target data in a dense vector space to enhance translation accuracy without relying on parallel text pairs.

<mark style="color:green;">**Retrieval Metrics**</mark>

* <mark style="color:blue;">**Sparse-vector Retrieval**</mark><mark style="color:blue;">:</mark> Techniques like TF-IDF and BM25, which rely on keyword matching, are used to fetch relevant instances based on lexical similarities.
* <mark style="color:blue;">**Dense-vector Retrieval**</mark><mark style="color:blue;">:</mark> This method retrieves semantically relevant instances, not just lexically similar ones, by representing text in dense vectors and computing retrieval scores through vector inner products.
* <mark style="color:blue;">**Task-specific Retrieval**</mark><mark style="color:blue;">:</mark> Rather than just relying on generic textual similarity, some methods optimise retrieval metrics for specific tasks, ensuring the retrieved content genuinely enhances the generation outcome.

<mark style="color:green;">**Integration Methods**</mark>

* <mark style="color:blue;">**Data Augmentation**</mark><mark style="color:blue;">:</mark> The retrieved content is combined with the original input to create augmented training instances, helping the model learn to utilize the retrieved information effectively.
* <mark style="color:blue;">**Attention Mechanisms**</mark><mark style="color:blue;">:</mark> Leveraging attention mechanisms allows the model to focus on and integrate useful information from the retrieved content, enhancing the generation process.
* <mark style="color:blue;">**Skeleton Extraction**</mark><mark style="color:blue;">:</mark> This approach involves extracting and integrating only the most relevant portions of the retrieved content, allowing the model to focus on useful information while discarding the irrelevant.

### <mark style="color:purple;">Challenges and methodologies in dialogue response generation</mark>

<mark style="color:green;">**Dialogue Systems Classification**</mark>

* <mark style="color:blue;">**Task-Oriented Systems**</mark><mark style="color:blue;">:</mark> These are designed to accomplish specific user tasks, like booking tickets.
* <mark style="color:blue;">**Chit-Chat Systems**</mark><mark style="color:blue;">:</mark> Aim to generate engaging and relevant responses without a fixed objective, facing the one-to-many problem where multiple responses can be suitable for a single dialogue history.

<mark style="color:green;">**Dialogue Response Generation Models**</mark>

* <mark style="color:blue;">**Retrieval-Based Models**</mark><mark style="color:blue;">:</mark> These models fetch an existing response from a dataset, ensuring informativeness and grammatical correctness. However, they struggle with unique dialogue histories not present in the dataset.
* <mark style="color:blue;">**Generation-Based Models**</mark><mark style="color:blue;">:</mark> Capable of generating new responses, these models offer better generalisation but often produce generic and less informative replies.

<mark style="color:green;">**Integration Approaches**</mark>

* <mark style="color:blue;">**Shallow Integration**</mark><mark style="color:blue;">:</mark> Early attempts combined retrieval and generation-based outputs, aiming to leverage the strengths of both. For instance, re-ranking outputs from both models was one such technique.
* <mark style="color:blue;">**Deep Integration**</mark><mark style="color:blue;">:</mark> More sophisticated methods integrate retrieval results directly into the generation process.  For example, some models use an additional encoder for the retrieval result or construct an edit vector to account for context differences between the dialogue history and the retrieved response. This approach aims to refine the generation process by incorporating relevant retrieved content.

<mark style="color:green;">**Knowledge-Enhanced Generation**</mark>

* Retrieval-augmented dialogue systems can also leverage external knowledge sources, not just dialogue corpora, to enrich responses. This inclusion of varied knowledge forms aims to produce more grounded and contextually appropriate responses.

<mark style="color:green;">**Limitations and Future Directions**</mark>

* The current dialogue response generation frameworks typically use a single retrieved response, potentially limiting the response's richness. Future research could explore integrating multiple retrieval responses.
* Customised retrieval metrics could offer more tailored and relevant responses, especially for generating responses with specific characteristics like persona or emotion.
* Expanding the retrieval pool beyond dialogue corpora to include diverse domains or modalities could provide a broader context and enhance the response generation process.


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