You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Scale is a key challenge for entity linking; there are millions of possible entities to consider for each mention. To efficiently filter or rank the candidates, existing methods use different sources of external information, including manually curated mention tables Ganea and Hofmann (2017), incoming Wikipedia link popularity Yamada et al. (2016), and gold Wikipedia entity categories Gillick et al. (2019). In this paper, we show that BERT-based models set new state-of-the-art performance levels for large scale entity linking when used in a zero shot setup, where there is no external knowledge and a short text description provides the only information we have for each entity. We also present an extensive evaluation of the accuracy-speed trade-off inherent to large pre-trained models, and show is possible to achieve very efficient linking with modest loss of accuracy. Our two-stage approach achieves a new state-of-the-art result on TACKBP-2010, with an over 30% relative error reduction. By simply reading the provided text descriptions, we are able to outperform previous methods that included many extra cues such as entity name dictionaries and link popularity. We also improve the state of the art on existing zero-shot benchmarks, including a nearly 6 point absolute gain on the recently introduced Wikia corpus Logeswaran et al. (2019) and more than 7 point absolute gain on WikilinksNED Unseen-Mentions Onoe and Durrett (2019). We proposed a conceptually simple, scalable, and highly effective two stage approach for entity linking. We show that our BERT-based model outperforms IR methods for entity retrieval, and achieved new state-of-the-art results on recently introduced zero-shot entity linking dataset, WikilinksNED Unseen-Mentions dataset, and the more established TACKBP-2010 benchmark, without any task-specific heuristics or external entity knowledge. We present evaluations of the accuracy-speed trade-off inherent to large pre-trained models, and show that it is possible to achieve efficient linking with modest loss of accuracy.Finally, we show that knowledge distillation can further improve bi-encoder model performance.Future work includes:•Enriching entity representations by adding entity type and entity graph information;•Modeling coherence by jointly resolving mentions in a document;•Extending our work to other languages and other domains;•Joint models for mention detection and entity linking. Two recent results are most closely related to our work. Logeswaran et al. (2019) proposed the zero-shot entity linking task. They use cross-encoders for entity ranking, but rely on traditional IR-techniques for candidate generation and did not evaluate on large scale benchmarks such as TACKBP.Gillick et al. (2019) show that dense embeddings work well for candidate generation, but they did not do pre-training and included external category labels in their bi-encoder architectures, limiting their linking to entities in Wikipedia. Our approach can be seen as generalizing both of these lines of work, and showing for the first time that pre-trained zero-shot architectures are both highly accurate and computationally efficient at scale.

Question: Why BLINK valuable?
The BLINK model can be said to be valuable since the model is simple yet scalable and effective compared to existing works. The proposed BERT-based model can perform entity linking with large-scale and zero-shot setups, which is crucial in real-world use cases that often contain a lot of unseen entities. BLINK also achieved a new state-of-the-art result for two zero-shot benchmarks by using only the provided text description without external knowledge, which shows the effectiveness of the proposed model.