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: The low-dimensional dense representations for query and passage are computed by PLMs based retrievers from the dual-encoder architecture. Afterward, the candidate passage set could be retrieved efficiently via approximate nearest neighbor algorithms.Existing studies could be categorized into two parts:(1) By optimizing the matching stage: DPR (Karpukhin et al., 2020) is the first study to leverage PLM to empower the retriever by a single vector. Other researches, such asRepBERT (Zhanet al., 2020), ColBERT (Khattab andZaharia, 2020), COIL (Gaoet al., 2021) and Interactor (Yeet al., 2022), obtain multiple vectors for query and passage for matching.(2) By optimizing the representation learning module: RocketQAv1 (Qu et al., 2021) and RocketQAv2 (Ren et al., 2021) boost the representation learning of retriever by leveraging the power of cross-encoder in a cascade or joint manner. Other studies boost the representation learning by designed IR-oriented pre-training tasks.ICT (Leeet al., 2019) treats sentences as pseudo-queries and matched them to the passage they originate from. Condenser (Gao and Callan, 2021) utilizes a novel pre-training task, which can produces an information-rich representation to condense an input sequence. We use the traditional sparse retriever BM25 (Yanget al., 2017) as our first stage method. All experiments are conducted under the same BM25 setting with 1000 retrieved candidates. We conduct experiments with the deep learning framework PaddlePaddle (Maet al., 2019) on up to 4 NVIDIA Tesla A100 GPUs (with 40G RAM). For the GMN module, we use Paddle Graph Learning (PGL) 222https://github.com/PaddlePaddle/PGL, an efficient and flexible graph learning framework based on PaddlePaddle. For training, we used the Adam optimizer (Kingma and Ba, 2014) with a learning rate of 1e-5 for text encoder and 1e-4 for knowledge injector. The model is trained up to 5 epochs with a batch size of 640 and 240 for base and large models respectively.In our experiments, the PLM small, base and large models have 6, 12 and 24 Transformer layers respectively.The text encoder has 9 layers and 21 layers for base and large model respectively, and the knowledge injector both has 3 layers in our experiment. The dropout rates are set to 0.1. The ratio of the positive to the hard negative is set to 1:19.All transformer layers in KERM’s backbone are initialized from ERNIE-2.0 base (Sunet al., 2020b), which is a BERT-like model pre-trained with a continual pre-training framework on multiple tasks. We perform Knowledge-enhanced pre-training on MARCO passage collection to warm up the parameters in knowledge injector, which has 60,000 iterations under the batch size of 256.For a fair comparison, the same pre-training without knowledge enhancement is also conducted on \textrm{ERNIE}_{\textrm{base}} re-ranker and all models in ablation studies. Given a query q, passage re-ranking aims at ordering a set of 𝜘 passages, i.e., P =  p𝜅 	𝜘 𝜅=1 , which is usually retrieved from a large-scale passage collection by a retriever, e.g. BM25 [48], DPR [16] etc. In particular, a passage is a sequence of words p = {𝑤𝑝 } |p| 𝑝=1 , where |p| is the length of passage p. Similarly, a query is a sequence of words q = {𝑤𝑞 } |q| 𝑞=1 . Note that a passage p consists of 𝑇 sentences p = {s𝜏 } 𝑇 𝜏=1.

Question: How is DPR retriever different from BM25?
BM25 and DPR are both examples of retrievers used in large-scale passage collection. BM25 is described as a traditional sparse retriever and DPR leverages PLM to empower the retriever by a single vector. How both BM25 and DPR function is not described in detail in this paper and thus their differences cannot be answered in this paper.