Effective Generative Retrieval through Term Set Generation

Core Concepts

A novel framework, Term-Set Generation (TSGen), that uses a set of terms as the document identifier (DocID) to address the false pruning problem in existing generative retrieval methods.

Abstract

The paper proposes a novel framework, Term-Set Generation (TSGen), for generative retrieval. Instead of using one or several sequences as the DocID, TSGen uses a set of terms that concisely summarize the document's semantics and distinguish it from others.
Key highlights:

The term-set DocID is selected based on learned weights from relevance signals, making it informative and discriminative.
TSGen introduces a permutation-invariant decoding algorithm that allows the model to explore the optimal permutation of the term set, which improves the likelihood of generating the relevant DocID.
The algorithm is resilient to decoding errors, as the relevant DocID will not be falsely pruned as long as the decoded terms belong to it.
An iterative optimization procedure is designed to incentivize the model to generate the relevant term set in its favorable permutation.
Extensive experiments on popular benchmarks validate the effectiveness, generalizability, scalability, and efficiency of TSGen compared to existing generative retrieval methods and traditional retrieval approaches.

Stats

TSGen outperforms the strongest generative retrieval baseline by +2% on M@100 on NQ320K and achieves a relative improvement of +16% over Ultron on MRR@100 on MS300K.
On unseen documents, TSGen significantly outperforms other generative retrieval baselines in terms of both MRR@10 and R@10, demonstrating superior generalizability.
On the large-scale MSMARCO Passage dataset, TSGen improves the MRR of DSI trained on the same amount of data and outperforms DSI scaled with 40 times more training data.

Quotes

"Instead of one or several sequences, TSGen uses a set of terms as the DocID. These terms are selected based on the learned weights from relevance signals, so that they not only concisely summarize the document's semantics, but distinguish the document from others."
"At each decoding step, TSGen perceives all valid terms rather than only the preceding ones, thereby acquiring full information about the DocID. Each term itself usually comprises only one token, thus the decoding space remains unchanged, and TSGen can make more reliable decisions given its broader perspective."
"TSGen is resilient to decoding errors. In contrast to sequence-based generation, the relevant DocID will not be falsely pruned as long as the decoded terms belong to it."

Key Insights Distilled From

Generative Retrieval via Term Set Generation

by Peitian Zhan... at arxiv.org 04-16-2024

https://arxiv.org/pdf/2305.13859.pdf

Deeper Inquiries

How can the term selection module be further improved to extract more informative and discriminative terms for the DocID

To further improve the term selection module in TSGen for extracting more informative and discriminative terms for the DocID, several strategies can be considered:

Incorporating Contextual Information: The term selection module can be enhanced by incorporating contextual information from the query and document. This can help in selecting terms that are more relevant to the specific context of the information retrieval task.

Utilizing Embeddings: Leveraging pre-trained word embeddings or contextual embeddings like BERT can aid in capturing semantic relationships between terms. By considering the embeddings of terms in the selection process, the module can identify terms that are semantically similar or related.

Fine-tuning with Reinforcement Learning: Training the term selection module using reinforcement learning techniques can optimize the selection process based on the performance of the generative retrieval model. By rewarding the selection of terms that lead to better retrieval results, the module can learn to extract more effective DocIDs.

Exploring Multi-granularity Representations: Instead of considering individual terms, the module can explore multi-granularity representations such as phrases, entities, or concepts. This can provide a richer representation of the document content and improve the discriminative power of the selected terms.

What are the potential drawbacks of the term-set DocID approach, and how can they be addressed

The term-set DocID approach in TSGen offers several advantages, such as mitigating the false pruning problem and enabling the model to explore optimal permutations for generating the relevant DocID. However, there are potential drawbacks that need to be addressed:

Scalability: As the corpus size increases, the number of term-set collisions may also increase, leading to challenges in maintaining uniqueness. Addressing this issue requires efficient strategies for handling collisions without compromising the quality of the term sets.

Interpretability: While the term-set DocID is effective for generative retrieval, it may lack interpretability compared to natural language sequences. Developing techniques to enhance the interpretability of the term sets can improve the understanding of the generated DocIDs.

Generalization: Ensuring that the model generalizes well to unseen documents is crucial for real-world applications. Techniques for enhancing the generalization capability of TSGen, such as data augmentation strategies or transfer learning approaches, can be explored.

Robustness: The term-set DocID approach may be sensitive to noise or irrelevant terms in the document. Implementing mechanisms to filter out noisy terms or improve the robustness of the term selection process can enhance the overall performance of TSGen.

How can the proposed techniques in TSGen be extended to other information retrieval tasks beyond document retrieval, such as question answering or dialogue systems

The techniques proposed in TSGen can be extended to other information retrieval tasks beyond document retrieval, such as question answering or dialogue systems, by adapting them to the specific requirements of these tasks:

Question Answering: In question answering tasks, the term-set generation approach can be applied to extract key terms from the question and relevant passages. By generating term sets that capture the essential information for answering the question, the model can improve the accuracy and relevance of the answers provided.

Dialogue Systems: For dialogue systems, the permutation-invariant decoding algorithm in TSGen can be utilized to generate response candidates based on the context of the conversation. By considering different permutations of terms in the dialogue history, the system can generate more coherent and contextually relevant responses.

Semantic Search: Extending TSGen to semantic search tasks involves generating term sets that capture the semantic relationships between queries and documents. By focusing on extracting terms that represent the underlying semantics of the information, the model can enhance the precision and relevance of search results in semantic search applications.