Dynamic Prefix as Instructor for Incremental Named Entity Recognition: A Unified Seq2Seq Generation Framework
Authors: Zihao Wu, YongXiang Hua, Yongxin Zhu, Fang Zhang, Linli Xu
Accepted at: acl25

Dynamic Prefix as Instructor for Incremental Named Entity Recognition:
A Unified Seq2Seq Generation Framework

Zihao Wu1,2, YongXiang Hua1,2, Yongxin Zhu1,2, Fang Zhang1,2, Linli Xu*1,2
1University of Science and Technology of China
2State Key Laboratory of Cognitive Intelligence
{zihaowu,yx15333063290,zyx2016,fangzhang}@mail.ustc.edu.cn
linlixu@ustc.edu.cn

Abstract

The Incremental Named Entity Recognition (INER) task aims to update a model to extract entities from an expanding set of entity type candidates due to concerns related to data privacy and scarcity. However, conventional sequence labeling approaches to INER often suffer from the catastrophic forgetting problem, which leads to the degradation of the model’s performance on previously encountered entity types. In this paper, we formalize INER as a unified seq2seq generation task and propose a parameter-efficient dynamic prefix method. By employing the Dynamic Prefix as a task Instructor (DPI) to guide the generative model, our approach can preserve task-invariant knowledge while adapting to new entities with minimal parameter updates, making it particularly effective in low-resource scenarios. Additionally, we introduce a generative label augmentation strategy with dual optimization objectives including a self-entropy loss and a task-aware similarity loss to enable optimal balance between stability and plasticity. Through extensive empirical evaluation on standard NER benchmarks, we demonstrate that our approach significantly outperforms existing methods, achieving up to 13.6% improvement in low-resource scenarios while maintaining strong performance on previously learned entity types.

1 Introduction

Named Entity Recognition (NER) is a fundamental problem in information extraction. Traditional NER systems typically require extensive annotated training data encompassing all predefined entity types. However, as new entity types emerge, re-training the entire model becomes impractical. Furthermore, obtaining sufficient supervised training data is challenging due to concerns related to data privacy and scarcity (Ma et al., 2020). Consequently, continual learning (or incremental learning) for NER has been proposed (Monaikul et al., 2021) as a solution to train the model incrementally on new datasets labeled exclusively with new entity types, addressing the issues associated with retraining and data availability.

While incremental learning aims to mirror human capability in continuously acquiring knowledge (Ke and Liu, 2022), it faces the challenge of catastrophic forgetting (McCloskey and Cohen, 1989), where models lose previously acquired knowledge while learning new tasks. This is particularly problematic in NER, where information about previous and future entity types is absent during current learning steps. Ma et al. (2023) identify that most INER errors arise from confusion between pre-defined entities and non-entities (“O”). As shown in Figure 1, the model that successfully learned to recognize “PER” (person) and “LOC” (location) in one step would be trained to annotate “PER” or “LOC” as “O” in the current and subsequent steps. At step t, only the entity type “MISC” (miscellaneous) is labeled, which leads to the wrong prediction of the entity “Croatia”. This indicates that the model has forgotten the entity information of “LOC” learned in previous tasks.

![Challenges in INER. At the current incremental step t, the data is only annotated with the current entity type [MISC], while previous entity types [LOC] and [PER] are annotated with [O]. [TIME] is a future entity type. “Current Pred” indicates that the model forgets previous entity type [LOC] after step t.](page_1047_370_377_180.png)

Figure 1: Challenges in INER. At the current incremental step t, the data is only annotated with the current entity type [MISC], while previous entity types [LOC] and [PER] are annotated with [O]. [TIME] is a future entity type. “Current Pred” indicates that the model forgets previous entity type [LOC] after step t.
Training directly on new data will exacerbate the background shift (Zhang et al., 2023) problem, where old and future entity types are mislabeled as the non-entity type. This results in a significant performance drop on historical entities. We validate the problem through experiments comparing three training paradigms (Figure 2). Multi-task learning (upperbound, green line) preserves all annotations, naive fine-tuning (blue line) shows catastrophic forgetting with F1 score plunging, while continual learning methods (orange line) maintain stable performance close to the upperbound.

Existing methods (Monaikul et al., 2021; Zheng et al., 2022; Zhang et al., 2023) treat INER as a sequence labeling classification task, which may encounter limitations, particularly in the era of Large Language Models (LLMs). Following traditional NER approaches, these methods use a text encoder to extract context representations, followed by a classification layer to assign entity types to individual tokens. This paradigm presents three critical challenges: 1. Structural Inflexibility: Adding new entity types requires expanding the classification layer and retraining the entire model, even when attempting to preserve existing weights. This architectural modification inevitably interferes with previously learned knowledge. 2. Parameter Inefficiency: The need to update both the encoder and classification layer parameters leads to significant computational overhead and increases the risk of catastrophic forgetting. 3. Limited Entity Modeling: The token-level classification paradigm struggles with complex scenarios such as nested or overlapping entities, often requiring specialized architectural modifications (Yan et al., 2021).

Motivated by these challenges, in this paper, we formalize INER as a seq2seq generation task, which aligns well with the generative nature of NER and facilitates prompt tuning more intuitively. Our proposed method employs a parameter-efficient dynamic prefix strategy tailored for incremental learning in INER. By dynamically assigning separate and task-specific prefixes as instructors during the incremental process, our model inspires the model to acquire new knowledge while retaining old prefixes to maintain stability. This structure inherently enables knowledge separation, where each prefix functions as a modular “expert” encoding specific entity-type patterns. During incremental learning, new prefixes are dynamically appended as lightweight instructors, while old prefixes remain intact, enabling parallel knowledge acquisition and retention. This approach is particularly effective for INER for two reasons: 1. Natural Entity Handling: The generative framework naturally supports complex scenarios such as overlapping entities by decoding entity spans autoregressively, overcoming the structural limitations of sequence labeling. 2. Efficient Knowledge Separation: The decoupled prefix architecture avoids overwriting shared parameters or expanding classification layers, ensuring smooth adaptation to new entity types.

Specifically, we integrate manually constructed task instructions and entity type options in the input sentence (as shown in Figure 3). Then, we introduce dynamic prefix as an instructor to guide the frozen Pre-trained Language Model (PLM) in learning new entity types. At each incremental step, we expand the prefix set while keeping new prefixes as the only trainable parameters (approximately 0.1% of the base model). All prefixes are pluggable and require no modifications to the base model. This results in significantly fewer parameters to fine-tune compared to prior INER methods. During inference, all prefixes collaborate to generate a sequence of entity types from current options and their corresponding entities. We further enhance the framework with a generation-based label augmentation strategy with a self-entropy loss and a task-aware similarity loss to achieve a more refined equilibrium between stability and plasticity.

Our main contributions are:

• We propose a dynamic prefix method to retain task-invariant capabilities and preserve task-specific knowledge in INER, requiring updates to only 0.1% of model parameters.

• We propose a generation-based label augmentation strategy with a self-entropy loss and a task-aware similarity loss, achieving an equilibrium between stability and plasticity.

• Comprehensive empirical validation shows significant improvements over existing methods, particularly in low-resource scenarios, while using orders of magnitude fewer parameters than traditional sequence labeling INER approaches.
Figure 2: An illustration of catastrophic forgetting. We conduct the comparison with three different settings on the CoNLL03 (Sang and De Meulder, 2003) dataset.

2 Related Work

2.1 Class-Incremental Learning

Prior approaches to class-incremental learning can be divided into three categories: (1) Architecture-based methods dynamically adjust the model architecture to learn new knowledge while mitigating the forgetting of previously learned tasks (Chen et al., 2016; Rusu et al., 2016; Mallya et al., 2018). (2) Regularization-based methods constrain the updates of parameters that are important to the learned tasks to retain previous knowledge (Li and Hoiem, 2017; Kirkpatrick et al., 2016; Aljundi et al., 2018). (3) Rehearsal-based methods keep exemplars from previous tasks in memory. (Lopez-Paz and Ranzato, 2017; Chaudhry et al., 2019; de Masson d’Autume et al., 2019).

2.2 Prompt Tuning in Continual Learning

As a lightweight alternative to fine-tuning, prompt-based methods often learn a prompt pool or a series of soft prompts to instruct the model while keeping the base model frozen (Wang et al., 2022b; Razdaibiedina et al., 2023; Wang et al., 2022a). These prompts serve as both task-invariant and task-specific instructions. When learning new tasks, the prompt pool is updated, or new prompts are introduced, ensuring the preservation of knowledge from previous tasks. Some works have already demonstrated that prompts can alleviate the problem of catastrophic forgetting to a certain extent (Smith et al., 2023). For instance, Razdaibiedina et al. (2023) propose Progressive Prompts and demonstrate their efficacy across 15 text classification tasks.

2.3 Incremental Named Entity Recognition

Monaikul et al. (2021) introduce the incremental learning paradigm into NER (i.e., INER) and propose AddNER and ExtendNER to alleviate catastrophic forgetting. L&R (Xia et al., 2022) adopts a replay-based approach to synthesize samples of old entity types. CFNER (Zheng et al., 2022) and RDP (Zhang et al., 2023) focus on extracting information from non-entity type and task relationships. Ma et al. (2023) proposes an entity-aware contrastive learning method that adaptively detects entity clusters in the “O” class. In line with CFNER and RDP, our method is rehearsal-free and does not keep any exemplars from previous tasks.

2.4 Generation based Named Entity Recognition

A seq2seq architecture is introduced with a pointer mechanism in Yan et al. (2021) to generate entity index sequences. Lu et al. (2022) introduce a universal information extraction model based on a unified generation structure. Chen et al. (2023) propose a collaborative prefix method based on the generative paradigm for knowledge transfer. However, in INER, it is essential to consider the performance not only in the target domain but also across all tasks. As a consequence, these methods show limited performance when directly applied to INER since they are not designed for incremental scenarios.

3 Methodology

In this section, we introduce our dynamic prefix method designed to facilitate INER by seq2seq generation framework. We start with providing a formalized definition of INER in Section 3.1, followed by the working mechanism of prefix tuning for NER in Section 3.2. In Section 3.3 we propose a dynamic prefix method as a task-invariant and task-specific instructor based on seq2seq generation framework. Finally, Section 3.4 outlines the strategy employed to achieve a balance between stability and plasticity of INER.

3.1 Problem Definition

Following previous works (Monaikul et al., 2021; Xia et al., 2022; Zheng et al., 2022; Zhang et al., 2023; Ma et al., 2023), we focus on class-incremental learning on NER (INER). Formally, INER contains \( N \) incremental steps, each associated with its corresponding task \( \{ \mathcal{T}_1, \mathcal{T}_2, \ldots, \mathcal{T}_N \} \).
Every task has its own dataset \( \{ \mathcal{D}_1, \mathcal{D}_2, \ldots, \mathcal{D}_N \} \). Specifically, the task at the \( t \)-th step can be described as \( \mathcal{T}_t = (\mathcal{D}_t^{tr}, \mathcal{D}_t^{dev}, \mathcal{D}_t^{test}, \mathcal{C}_t^{new}, \mathcal{C}_t^{old}) \), where \( \mathcal{C}_t^{new} \) is the label set (i.e., new entity types) of the current task (e.g., {“PER”, “ORG”}) and \( \mathcal{C}_t^{old} = \bigcup_{i=1}^{t-1} \mathcal{C}_i^{new} \) represents the label set containing all seen entity types in old tasks. Each task has its unique training set \( \mathcal{D}_t^{tr} = \{ X_t^j, Y_t^j \}_{j=1}^n \), where \( X_t^j = \{ x_t^{j,1}, \ldots, x_t^{j,l} \} \) (with \( l \) as the sequence length) and \( Y_t^j = \{ y_t^{j,1}, \ldots, y_t^{j,l} \} \), \( y_t^{j,k} \in \mathcal{C}_t^{new} (k = 1, \ldots, l) \) are annotated with only the new entity types or “O”. At step \( t \), with the model \( \mathcal{M}_{t-1} \) trained at step \( t-1 \), we update \( \mathcal{M}_{t-1} \) at \( \mathcal{T}_t \) in order to train a model \( \mathcal{M}_t \) which is expected to perform well on all seen entity types \( \mathcal{C}_t^{all} = \mathcal{C}_t^{new} \cup \mathcal{C}_t^{old} \).

3.2 Prefix Tuning for Seq2Seq Generation in Named Entity Recognition

Prompt-based learning has been widely applied in NLP tasks, especially with the rise of LLMs. By providing manually designed hard prompts or attaching a set of soft prompts, they can serve as instructions for Pre-Trained Language Models (PLMs) in downstream tasks.

Specifically, given the input \( (X^j, Y^j) \in \mathcal{D}^{tr} \), a sequence of soft prompts can be prepended to each layer of the transformer to obtain the input as: \( Z^j = [\text{PREFIX}; X^j; \text{PREFIX}'; Y^j] \) (Li and Liang, 2021). The activations of the prefix are always in the left context and will therefore affect subsequent activations to the right.

Based on prompt-based learning, we tackle the NER problem in a seq2seq paradigm, which offers an intuitive framework for integrating prompt-based techniques. Figure 3 shows the unified seq2seq procedure. The trainable prefixes serve as a guide for the seq2seq model, prompting it to extract all entities and the corresponding entity types in the input sentence. Formally, given the manually constructed task instruction (s) specific to NER, at each step \( t \) the model takes the input sentence \( X_t \) with the entity type options (\( o_t \)), and generates a sequence \( \hat{y}_t \) which is expected to contain all entity types and their corresponding entities:

\[
\hat{y}_t = \mathrm{LM}_{\phi, \theta}(s; o_t; X_t),
\]

where the language model parameters \( \phi \) are frozen and the prefix parameters \( \theta \) are the only trainable parameters in our continual steps. Note that we can obtain the label sequence \( \hat{y}_t \) by post-processing the original output \( \hat{y}_t \).

3.3 Dynamic Prefix

When it comes to the incremental setting, the objective of the seq2seq INER is:

\[
\max_{\theta} \sum_{t=1}^N \sum_{(x,y) \in \mathcal{T}_t} \log p(y|x, \phi, \theta)
\]

To adapt our method to the incremental setting, we propose a *Dynamic Prefix* method as illustrated in Figure 4. We dynamically increase the number of prefixes which are expected to learn task-specific knowledge. Simultaneously, by concatenating newly added prefixes with the existing ones, we prevent forgetting knowledge pertaining to previous entity types, while adapting to new entities with minimal parameter updates and maximal knowledge acquisition. Specifically, when training the incremental task \( \mathcal{T}_t \), a set of new prefixes \( \mathbf{P}_t \in \mathbb{R}^{|L_t| \times d} \) with length of \( |L_t| \) parameterized by \( \theta_t \) are inserted into each layer while keeping the LM parameters (\( \phi \)) and all old prefix parameters (\( \theta_1, \ldots, \theta_{t-1} \)) frozen. The objective of our dynamic prefix approach at step \( t \) becomes:

\[
\max_{\theta_t} \sum_{(x,y) \in \mathcal{T}_t} \log p(y|x, \phi, \theta_1, \ldots, \theta_t)
\]

![An illustration of the unified seq2seq approach for NER.](page_186_120_1077_246.png)

Figure 3: An illustration of the unified seq2seq approach for NER.
Figure 4: The overall architecture of our proposed DPI for INER. Here “+” denotes the concatenation operation.

As shown in Figure 4, we concatenate the new prefixes with the old prefixes along the prefix length dimension. Then the entire set of prefixes \( \mathbf{P} \) is split into \( \mathbf{P}_k \) and \( \mathbf{P}_v \), which are concatenated with the original keys \( \mathbf{K} \) and values \( \mathbf{V} \) to compute each head vector. The computation of the \( i \)-th head vector \( \mathrm{head}_i \) can be written as:

\[
\mathrm{head}_i = \mathrm{Attn}(xW_q^{(i)}, [\mathbf{P}_k^{(i)}; CW_k^{(i)}], [\mathbf{P}_v^{(i)}; CW_v^{(i)}]) \tag{4}
\]

The activation vector \( h_i \in \mathbb{R}^d \) at time step \( i \) is computed as:

\[
h_i = \begin{cases}
\mathbf{P}[i,:], & \text{if } i \in L \\
\mathrm{LM}_\phi(Z_i, h_{<i}), & \text{otherwise}
\end{cases} \tag{5}
\]

where \( \mathbf{P} \in \mathbb{R}^{[L] \times d} \) is a partially trainable matrix with \( L = [L_1; \ldots; L_k; \ldots; L_t] \). \( L_k \) denotes the sequence of prefix indices of new prefixes at incremental step \( k \).

Then we optimize the new prefix parameters \( \theta_t \) by minimizing the negative log-likelihood over the training set \( D_t^{tr} \) of task \( \mathcal{T}_t \).

\[
\mathcal{L}_{\mathrm{nl}}(\theta_t) = - \sum_{(x, y) \in D_t^{tr}} \log p(y|\{\mathbf{P}_t, \ldots, \mathbf{P}_1, x\}, \phi, \theta_1, \ldots, \theta_t) \tag{6}
\]

where the only trainable parameters are \( \theta_t \) related to new prefixes.

3.4 Equilibrium Between Stability and Plasticity

In this section, we introduce the strategy employed to achieve a balance between stability and plasticity of INER.

**Label Augmentation Strategy.** The entities annotated with “O” at the current step may belong to the previous entity types \( C_t^{old} \) or the future entity types \( \bigcup\limits_{i=t+1}^N C_i^{new} \). Obviously, the future entity types cannot be seen in the current task. For entities that belong to \( C_t^{old} \), we employ a generation-based label augmentation strategy. This strategy leverages the capabilities of the old model. By leveraging the old entity type information contained in tokens annotated with “O”, the stability is enhanced when learning new entity types. Before training each task, we utilize the old model \( \mathcal{M}^{t-1} \) to predict a “pseudo” entity type for entities annotated with “O”. The augmented labels are then fused with the current labels for training the current task. As mentioned above, the original true label of the current task is denoted as \( Y_t^j = \{y_t^{j,1}, \ldots, y_t^{j,t}\} \). To obtain the augmented label \( \tilde{y}_t^{j,k} \) for the \( k^{th} \) token of the \( j^{th} \) input, we employ the strategy as follows:

\[
\tilde{y}_t^{j,k} = \begin{cases}
\hat{y}_t^{j,k-1}, & \text{if } y_t^{j,k} = "O" \\
y_t^{j,k}, & \text{otherwise}
\end{cases} \tag{7}
\]
where

\[
\hat{y}_{t-1}^j = \arg\max_{o \in o_{t-1}} \mathcal{M}_{t-1}(s; o_{t-1}; X_t) \tag{8}
\]

After applying the label augmentation strategy, we obtain the final training set \( \mathcal{D}_t^{tr} = \{X_t^j, Y_t^j\}_{j=1}^n \) for the current step \( t \). The label augmentation strategy is expected to enhance the stability of our model. Then the Equation (6) can be formulated as follows:

\[
\mathcal{L}_{\text{nll}}(\theta_t) \\
= - \sum_{(x, y) \in \mathcal{D}_t^{tr}} \log p(y|[\mathbf{P}_t, \ldots, \mathbf{P}_1, x], \phi, \theta_1, \ldots, \theta_t) \tag{9}
\]

**Self-entropy Loss.** To further extend the model’s plasticity, we introduce a self-entropy loss to encourage the model to make more confident predictions. We minimize the self-entropy loss to promote the model’s confidence in learning the new entity types:

\[
\mathcal{L}_{\text{se}} = - \frac{1}{l} \sum_{k=1}^l \hat{y}^k \log \hat{y}^k \tag{10}
\]

Here \( \hat{y}^k \) denotes the output probability distribution.

**Task-aware Similarity Loss.** To maintain distinction across tasks while enabling effective knowledge transfer, we introduce a dynamic regularization mechanism for prefix parameter optimization. The key insight is that tasks with similar entity types require more careful parameter separation to prevent interference. We quantify this through task-level semantic similarity, computed from entity type representations for simplicity. Specifically, we first obtain the representation \( c_i \) of the entity type definition \( c_i \) (as shown in Appendix A.2).

\[
c_i = \text{Encoder}(c_i) \in \mathbb{R}^d \tag{11}
\]

For a task \( t \) containing \( K \) entity types, we compute its task-level semantic representation \( \mathbf{T}_t \) by aggregating individual entity type embeddings:

\[
\mathbf{T}_t = \frac{1}{K} \sum_{i=1}^K c_i \tag{12}
\]

Then we minimize the task-aware similarity loss to adaptively regulate the optimization of new prefix parameters based on semantic overlap with previous tasks.

\[
\mathcal{L}_{\text{sim}} = \sum_{i=1}^{t-1} \max(0, \cos(\mathbf{T}_t, \mathbf{T}_i)) \text{Sim}(\mathbf{P}_t, \mathbf{P}_i)^2 \tag{13}
\]

In summary, the objective function of our proposed method is:

\[
\mathcal{L}_{\text{overall}} = \mathcal{L}_{\text{nll}} + \lambda_1 \mathcal{L}_{\text{se}} + \lambda_2 \mathcal{L}_{\text{sim}} \tag{14}
\]

4 **Experiment**

4.1 **Experimental Settings**

**Datasets.** We conduct experiments on three widely used NER dataset: CoNLL03 (Sang and De Meulder, 2003), I2B2 (Murphy et al., 2010) and OntoNotes5 (Hovy et al., 2006) for evaluating the effectiveness of our method. The dataset statistics are shown in Table 7 in Appendix A.1. Following CFNER (Zheng et al., 2022), for each dataset, a greedy sampling strategy is adopted to partition the training set into disjoint slices to better simulate realistic scenarios. Each slice corresponds to an incremental step. Specifically, \( FG \) entity types are used to train the initial model, and \( PG \) entity types are used for training in each subsequent incremental step. For example, under the “FG-8-PG-2” setting, 8 entity types are annotated in the first step and 2 entity types are annotated in each subsequent step. After dividing the original dataset into slices, we utilize UIE\( ^1 \) for data pre-processing. Finally, the data annotated with “BIO” schema is converted into the UIE format (Lu et al., 2022) (i.e., the “Data Format” module in Figure 4) for seq2seq generation.

**Training.** Different from previous works (Zheng et al., 2022; Zhang et al., 2023) using BERT-base (Devlin et al., 2018) for INER, we use T5-base (Raffel et al., 2019) as the backbone model for INER via seq2seq generation. Instead of fine-tuning almost all parameters, including the backbone model, at each incremental step as in previous methods, our dynamic prefix tuning method keeps the parameters of the backbone model frozen. The pluggable new prefixes are the only trainable parameters (approximately 0.1% of the backbone model). The implementation details can be found in Appendix B.

**Baselines.** We compare our method (DPI) with representative INER methods, including Extend-NER (Monaikul et al., 2021), CFNER (Zheng et al.,

1https://github.com/universal-ie/UIE
<table>
  <tr>
    <th rowspan="2">Method</th>
    <th rowspan="2">Trainable Param.</th>
    <th colspan="4">Task ID</th>
    <th rowspan="2">Avg.</th>
  </tr>
  <tr>
    <th>t=1 [LOC]</th>
    <th>t=2 +[MISC]</th>
    <th>t=3 +[ORG]</th>
    <th>t=4 +[PER]</th>
  </tr>
  <tr>
    <td>Directly Fine-tune Full Data</td>
    <td>~0.1% of 220M</td>
    <td>86.14</td>
    <td>35.83</td>
    <td>41.85</td>
    <td>41.97</td>
    <td>51.45</td>
  </tr>
  <tr>
    <td></td>
    <td></td>
    <td>86.14</td>
    <td>87.99</td>
    <td>87.98</td>
    <td>89.97</td>
    <td>88.02</td>
  </tr>
  <tr>
    <td>PODNet (Douillard et al., 2020)</td>
    <td></td>
    <td>85.96</td>
    <td>11.13</td>
    <td>24.16</td>
    <td>25.49</td>
    <td>36.74</td>
  </tr>
  <tr>
    <td>LUCIR (Hou et al., 2019)</td>
    <td></td>
    <td>85.96</td>
    <td>73.85</td>
    <td>62.81</td>
    <td>73.78</td>
    <td>74.15</td>
  </tr>
  <tr>
    <td>ExtendNER (Monaikul et al., 2021)</td>
    <td>~100% of 110M</td>
    <td>85.96</td>
    <td>74.42</td>
    <td>69.27</td>
    <td>75.78</td>
    <td>76.36</td>
  </tr>
  <tr>
    <td>CFNER (Zheng et al., 2022)</td>
    <td></td>
    <td>85.96</td>
    <td>80.63</td>
    <td>76.10</td>
    <td>80.95</td>
    <td>80.91</td>
  </tr>
  <tr>
    <td>RDP* (Zhang et al., 2023)</td>
    <td></td>
    <td>84.53</td>
    <td>77.31</td>
    <td>76.67</td>
    <td>79.22</td>
    <td>79.43</td>
  </tr>
  <tr>
    <td>DPI (Ours)</td>
    <td>~0.1% of 220M</td>
    <td><b>86.14</b></td>
    <td><b>82.10</b></td>
    <td><b>76.81</b></td>
    <td><b>81.46</b></td>
    <td><b>81.63</b></td>
  </tr>
</table>

Table 1: Main results of the proposed method and baselines under the FG-1-PG-1 setting of the CoNLL03 dataset (Sang and De Meulder, 2003). Micro-F1 score is reported. * represents results from the provided code. Other baseline results are directly cited from CFNER (Zheng et al., 2022).

<table>
  <tr>
    <th>Method</th>
    <th>Trainable Param.</th>
    <th>t=1</th>
    <th>t=2</th>
    <th>t=3</th>
    <th>t=4</th>
    <th>t=5</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td>PODNet (Douillard et al., 2020)</td>
    <td></td>
    <td>89.53</td>
    <td>28.50</td>
    <td>22.89</td>
    <td>21.86</td>
    <td>18.32</td>
    <td>36.22</td>
  </tr>
  <tr>
    <td>LUCIR (Hou et al., 2019)</td>
    <td></td>
    <td>90.23</td>
    <td>72.00</td>
    <td>63.18</td>
    <td>60.96</td>
    <td>56.32</td>
    <td>68.54</td>
  </tr>
  <tr>
    <td>ExtendNER (Monaikul et al., 2021)</td>
    <td>~100% of 110M</td>
    <td>89.39</td>
    <td>53.84</td>
    <td>42.25</td>
    <td>39.31</td>
    <td>36.47</td>
    <td>52.25</td>
  </tr>
  <tr>
    <td>CFNER (Zheng et al., 2022)</td>
    <td></td>
    <td>89.39</td>
    <td>70.29</td>
    <td>64.10</td>
    <td>62.01</td>
    <td>59.58</td>
    <td>69.07</td>
  </tr>
  <tr>
    <td>RDP* (Zhang et al., 2023)</td>
    <td></td>
    <td>90.94</td>
    <td>77.86</td>
    <td>69.16</td>
    <td>63.95</td>
    <td>53.36</td>
    <td>71.05</td>
  </tr>
  <tr>
    <td>DPI (Ours)</td>
    <td>~0.1% of 220M</td>
    <td><b>91.43</b></td>
    <td><b>84.98</b></td>
    <td><b>75.92</b></td>
    <td><b>72.51</b></td>
    <td><b>72.08</b></td>
    <td><b>79.38</b></td>
  </tr>
</table>

Table 2: Comparison with baselines under the FG-8-PG-2 setting of the I2B2 dataset (Murphy et al., 2010). Micro-F1 score is reported. * represents results from the provided code. Other baseline results are directly cited from CFNER (Zheng et al., 2022).

2022), and RDP (Zhang et al., 2023). Additionally, PODNet (Douillard et al., 2020) and LUCIR (Hou et al., 2019) are adapted to INER scenario by Zheng et al. (2022). We re-implement RDP which is the previous state-of-the-art INER method, while the results of the other baselines are directly cited from CFNER (Zheng et al., 2022).

5 Results and Discussion

5.1 Main Results

We conduct experiments under INER settings and present the quantitative task-wise performance compared to the baselines.

As shown in Table 1, the Full Data results, where all the seen entity types are annotated, are relatively stable, serving as an upperbound of our method. Directly Fine-tune represents the naive method where no incremental techniques are utilized, resulting in a sharp decline in performance. However, all the incremental learning methods show varying degrees of forgetting during the incremental process. Compared to the previous SOTA baselines CFNER (Zheng et al., 2022) and RDP (Zhang et al., 2023), our method demonstrates improvements in both average and task-wise results of CoNLL03 (Sang and De Meulder, 2003) under the FG-1-PG-1 INER setting.

To simulate a realistic scenario allowing the model to acquire sufficient “base knowledge” before incremental learning, we conduct experiments where we initially learn half of all entity types. The results of I2B2 (Murphy et al., 2010) and Ontonotes (Hovy et al., 2006) under FG-8-PG-2 are summarized in Table 2 and Table 3, demonstrating an improvement of approximately 8.3% and 0.5% respectively compared to RDP.

To delve deeper into the performance of DPI, we conduct experiments with a broader range of incremental steps. As depicted in Figure 5, under the FG-2-PG-2 setting of I2B2, a total of 8 steps are considered. The performance of CFNER (Zheng et al., 2022) declines significantly with deeper incremental steps. However, our method consistently outperforms the previous SOTA method RDP (Zhang et al., 2023) throughout the incremental steps. Figure 5 indicates that our method outperforms significantly over the previous methods when encountering more entity types and incremental steps. These quantitative results indicate that our proposed method can achieve better performance and alleviate catastrophic forgetting by fine-tuning significantly fewer parameters.

5.2 Low-resource Settings

Due to concerns related to data privacy and scarcity in realistic applications, INER often encounters low-resource scenarios. To further investigate the effectiveness of our method regarding the data scale, we conduct experiments on various datasets with low-resource settings. We report the results
<table>
  <tr>
    <th>Method</th>
    <th>Trainable Param.</th>
    <th>t=1</th>
    <th>t=2</th>
    <th>t=3</th>
    <th>t=4</th>
    <th>t=5</th>
    <th>t=6</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td>PODNet (Douillard et al., 2020)</td>
    <td></td>
    <td>82.52</td>
    <td>29.44</td>
    <td>23.42</td>
    <td>32.19</td>
    <td>29.36</td>
    <td>27.33</td>
    <td>37.38</td>
  </tr>
  <tr>
    <td>LUCIR (Hou et al., 2019)</td>
    <td></td>
    <td>82.67</td>
    <td>78.80</td>
    <td>75.43</td>
    <td>76.14</td>
    <td>75.03</td>
    <td>68.94</td>
    <td>76.17</td>
  </tr>
  <tr>
    <td>ExtendNER* (Monaikul et al., 2021)</td>
    <td>~100% of 110M</td>
    <td>82.37</td>
    <td>79.56</td>
    <td>75.24</td>
    <td>76.93</td>
    <td>76.40</td>
    <td>73.36</td>
    <td>77.31</td>
  </tr>
  <tr>
    <td>CFNER* (Zheng et al., 2022)</td>
    <td></td>
    <td>82.37</td>
    <td>82.10</td>
    <td>79.16</td>
    <td>80.51</td>
    <td>79.30</td>
    <td>76.63</td>
    <td>80.01</td>
  </tr>
  <tr>
    <td>RDP* (Zhang et al., 2023)</td>
    <td></td>
    <td>85.01</td>
    <td>83.68</td>
    <td>82.08</td>
    <td><b>83.26</b></td>
    <td><b>82.43</b></td>
    <td>79.1</td>
    <td>82.59</td>
  </tr>
  <tr>
    <td>DPI (Ours)</td>
    <td>~0.1% of 220M</td>
    <td><b>86.13</b></td>
    <td><b>84.21</b></td>
    <td><b>83.45</b></td>
    <td>83.07</td>
    <td>81.49</td>
    <td><b>80.28</b></td>
    <td><b>83.11</b></td>
  </tr>
</table>

Table 3: Comparison with baselines under the FG-8-PG-2 setting of the Ontonotes5 dataset (Hovy et al., 2006). Micro-F1 score is reported. * represents results from the provided code. Other baseline results are directly cited from CFNER (Zheng et al., 2022).

![Line graph showing task-wise results on the I2B2 dataset](page_184_624_482_312.png)

Figure 5: Task-wise results on the I2B2 (Murphy et al., 2010) dataset.

on the OntoNotes5 (Hovy et al., 2006) and the CoNLL03 (Sang and De Meulder, 2003) dataset in Table 4 and Table 5, respectively. For each incremental step, we respectively sample 5% and 10% of the training set while adopting a greedy sampling strategy to partition the training set. We compare our DPI method with the previous SOTA approach RDP (Zhang et al., 2023). In a low-resource scenario with only 10% of the data available, our DPI method improves over RDP by approximately 1.8% and 13.6% on OntoNotes5 and ConLL03, respectively. In a more stringent low-resource scenario, our method also outperforms RDP by approximately 3.7% and 12.2%. In comparison, our approach maintains the ability to identify entities effectively, by fine-tuning significantly fewer parameters at each step, and effectively capturing the patterns of different entity types in low-resource scenarios.

<table>
  <tr>
    <th>Rate</th>
    <th>Method</th>
    <th>t=1</th>
    <th>t=2</th>
    <th>t=3</th>
    <th>t=4</th>
    <th>t=5</th>
    <th>t=6</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td>10%</td>
    <td>DPI (Ours)</td>
    <td>79.53</td>
    <td>75.54</td>
    <td>72.07</td>
    <td>76.44</td>
    <td>72.61</td>
    <td>70.59</td>
    <td><b>74.46</b></td>
  </tr>
  <tr>
    <td></td>
    <td>RDP</td>
    <td>78.50</td>
    <td>74.79</td>
    <td>70.92</td>
    <td>73.43</td>
    <td>70.10</td>
    <td>68.25</td>
    <td>72.60</td>
  </tr>
  <tr>
    <td>5%</td>
    <td>DPI (Ours)</td>
    <td>72.79</td>
    <td>70.55</td>
    <td>63.81</td>
    <td>66.38</td>
    <td>66.09</td>
    <td>65.49</td>
    <td><b>67.85</b></td>
  </tr>
  <tr>
    <td></td>
    <td>RDP</td>
    <td>68.70</td>
    <td>62.01</td>
    <td>62.28</td>
    <td>65.32</td>
    <td>65.17</td>
    <td>61.50</td>
    <td>64.16</td>
  </tr>
</table>

Table 4: Performance in low-resource conditions on the OntoNotes5 (Hovy et al., 2006) dataset under the FG-8-PG-2 INER setting.

<table>
  <tr>
    <th>Rate</th>
    <th>Method</th>
    <th>t=1</th>
    <th>t=2</th>
    <th>t=3</th>
    <th>t=4</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td>10%</td>
    <td>DPI (Ours)</td>
    <td>60.63</td>
    <td>50.85</td>
    <td>55.07</td>
    <td>66.90</td>
    <td><b>58.36</b></td>
  </tr>
  <tr>
    <td></td>
    <td>RDP</td>
    <td>52.81</td>
    <td>45.27</td>
    <td>35.03</td>
    <td>45.85</td>
    <td>44.74</td>
  </tr>
  <tr>
    <td>5%</td>
    <td>DPI (Ours)</td>
    <td>55.67</td>
    <td>51.17</td>
    <td>55.05</td>
    <td>61.92</td>
    <td><b>55.95</b></td>
  </tr>
  <tr>
    <td></td>
    <td>RDP</td>
    <td>54.00</td>
    <td>36.27</td>
    <td>39.73</td>
    <td>45.03</td>
    <td>43.76</td>
  </tr>
</table>

Table 5: Performance in low-resource conditions on the CoNLL03 (Sang and De Meulder, 2003) dataset under the FG-1-PG-1 INER setting.

shown in Table 6, all ablation factors degrade the INER performance of DPI. DPI w/o DP represents our method without the dynamic prefix strategy, where prefixes with fixed size are trained throughout the incremental process. The results indicate that prefixes with fixed size lack the continual ability, which is exacerbated with more incremental steps. DPI w/o LAS means no label augmentation strategy is employed. By employing LAS and introducing self-entropy loss and task-aware similarity loss, we further achieve an equilibrium between stability and plasticity.

<table>
  <tr>
    <th rowspan="2">Method</th>
    <th colspan="2">CoNLL03</th>
    <th colspan="2">I2B2</th>
  </tr>
  <tr>
    <th>FG-1-PG-1</th>
    <th>FG-2-PG-1</th>
    <th>FG-2-PG-2</th>
    <th>FG-8-PG-2</th>
  </tr>
  <tr>
    <td>DPI</td>
    <td>81.63</td>
    <td>83.10</td>
    <td>80.16</td>
    <td>79.38</td>
  </tr>
  <tr>
    <td>w/o DP</td>
    <td>76.48</td>
    <td>79.33</td>
    <td>71.28</td>
    <td>72.64</td>
  </tr>
  <tr>
    <td>w/o \( \mathcal{L}_{ce} \)</td>
    <td>80.96</td>
    <td>81.23</td>
    <td>76.02</td>
    <td>74.80</td>
  </tr>
  <tr>
    <td>w/o \( \mathcal{L}_{sim} \)</td>
    <td>81.19</td>
    <td>82.70</td>
    <td>78.82</td>
    <td>78.58</td>
  </tr>
  <tr>
    <td>w/o LAS</td>
    <td>59.45</td>
    <td>61.40</td>
    <td>54.26</td>
    <td>57.03</td>
  </tr>
</table>

Table 6: Ablation study of our DPI method. The average Micro-F1 score is reported.

5.3 Ablation Studies

We conduct ablation studies to analyze the factors influencing the performance of our method. As shown in Table 6, all ablation factors degrade the INER performance of DPI. DPI w/o DP represents our method without the dynamic prefix strategy, where prefixes with fixed size are trained throughout the incremental process. The results indicate that prefixes with fixed size lack the continual ability, which is exacerbated with more incremental steps. DPI w/o LAS means no label augmentation strategy is employed. By employing LAS and introducing self-entropy loss and task-aware similarity loss, we further achieve an equilibrium between stability and plasticity. Empirical experiments on the INER benchmark demonstrate the effectiveness

6 Conclusion

In this work, we introduce a dynamic prefix method and formalize INER as a seq2seq generation task. By employing the dynamic prefix based on a seq2seq generation framework, our method retains task-invariant capabilities and preserves task-specific knowledge in INER. Additionally, we propose a generation-based label augmentation strategy with a self-entropy loss and a task-aware similarity loss to achieve a refined equilibrium between stability and plasticity. Empirical experiments on the INER benchmark demonstrate the effectiveness
of our proposed method. We further evaluate our method on various datasets with low-resource settings, and the results indicate the robustness and practicality of our method in more realistic scenarios with limited training data. This work also provides a potential direction that addresses the INER task more naturally in a generative manner.

7 Limitations

The limitations of this work include: (1) More complex NER problems are not considered in this work, such as coarse-to-fine INER. Our approach is not designed to address the problem that a new entity type might be entailed in an old entity type, for example, “Doctor” emerging after “Person”. Additionally, while our seq2seq generation framework is capable of addressing nested or discontinuous NER problems, we do not evaluate its performance on nested or discontinuous NER datasets due to the absence of suitable split algorithms for the incremental setting. (2) Our proposed label augmentation strategy relies on the old model to predict “pseudo” entity types, which may lead to error propagation. More refined label augmentation strategies will be explored in our future work.

Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant No. 62276245).

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A Dataset

A.1 Dataset Statistics
The dataset statistics are shown in Table 7.

A.2 Entity Type Definition
Entity types and their definitions are illustrated in Table 8.

B Implementation Details
The model is implemented in the PyTorch framework on top of the T5 Huggingface implementation. Consistent with RDP, we train the model for 20 epochs if PG=2, and 10 epochs otherwise. The learning rate, batch size, prompt length, prompt hidden dim, \( \lambda_1 \) and \( \lambda_2 \) is set to 7e-5, 32, 10, 1024, 0.1 and 0.2 respectively. All experiments are conducted on a single NVIDIA GeForce RTX 3090 GPU with 24GB of memory.

C Additional Experimental Results

C.1 Visualization of Entity Type Similarity
Figure 6, Figure 7 and Figure 8 show the entity type similarity on the Ontonotes5 (Hovy et al., 2006), the I2B2 (Murphy et al., 2010) and the CoNLL03 (Sang and De Meulder, 2003) dataset.

![Entity type similarity of the Ontonotes5 (Hovy et al., 2006) dataset.](page_184_1012_670_370.png)
Figure 6: Entity type similarity of the Ontonotes5 (Hovy et al., 2006) dataset.

![Entity type similarity of the I2B2 (Murphy et al., 2010) dataset.](page_820_1012_670_370.png)
Figure 7: Entity type similarity of the I2B2 (Murphy et al., 2010) dataset.

![Entity type similarity of the CoNLL03 (Sang and De Meulder, 2003) dataset.](page_184_1402_670_370.png)
Figure 8: Entity type similarity of the CoNLL03 (Sang and De Meulder, 2003) dataset.

C.2 Category-wise Results
To illustrate the performance variation of a single category throughout the incremental process, we present the results of four categories ("DATE", "EVENT", "GPE", "LAW") at step 1 in Figure 9, under the FG-8-PG-2 setting of the Ontonotes5 (Hovy et al., 2006) dataset.

C.3 Low-resource Results
The low-resource result of the I2B2 (Murphy et al., 2010) dataset is shown in Table 9. On the I2B2 dataset, RDP consistently fails to recognize almost all entities at every step. A possible reason is that it fine-tunes nearly all parameters during the incremental process, which hampers its ability to extract useful information when training data is limited.
<table>
  <tr>
    <th>Dataset</th>
    <th># Entity Type</th>
    <th># Sample</th>
    <th>Entity Type</th>
  </tr>
  <tr>
    <td>CoNLL03</td>
    <td>4</td>
    <td>21k</td>
    <td>LOCATION, MISC, ORGANISATION, PERSON</td>
  </tr>
  <tr>
    <td>I2B2</td>
    <td>16</td>
    <td>141k</td>
    <td>AGE, CITY, COUNTRY, DATE, DOCTOR, HOSPITAL, IDNUM, MEDICALRECORD, ORGANIZATION, PATIENT, PHONE, PROFESSION, STATE, STREET, USERNAME, ZIP</td>
  </tr>
  <tr>
    <td rowspan="2">OntoNotes5</td>
    <td rowspan="2">18</td>
    <td rowspan="2">77k</td>
    <td>CARDINAL, DATE, EVENT, FAC, GPE, LANGUAGE,</td>
  </tr>
  <tr>
    <td>LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, QUANTITY, TIME, WORK_OF_ART</td>
  </tr>
</table>

Table 7: Statistics of the NER datasets CoNLL03 (Sang and De Meulder, 2003), I2B2 (Murphy et al., 2010) and OntoNotes5 (Hovy et al., 2006).

<table>
  <tr>
    <th>Entity type</th>
    <th>Definition</th>
  </tr>
  <tr>
    <td>CARDINAL</td>
    <td>Numerals that do not fall under another type.</td>
  </tr>
  <tr>
    <td>DATE</td>
    <td>Absolute or relative dates or periods.</td>
  </tr>
  <tr>
    <td>EVENT</td>
    <td>Named hurricanes, battles, wars, sports events, etc.</td>
  </tr>
  <tr>
    <td>FAC</td>
    <td>Facility (Buildings, airports, highways, bridges, etc.).</td>
  </tr>
  <tr>
    <td>GPE</td>
    <td>Geopolitical entities: countries, cities, states.</td>
  </tr>
  <tr>
    <td>LANGUAGE</td>
    <td>Any named language.</td>
  </tr>
  <tr>
    <td>LAW</td>
    <td>Named documents made into laws.</td>
  </tr>
  <tr>
    <td>LOC</td>
    <td>Locations excluding geopolitical entities, mountain ranges, bodies of water.</td>
  </tr>
  <tr>
    <td>MONEY</td>
    <td>Monetary values, including currency units.</td>
  </tr>
  <tr>
    <td>NORP</td>
    <td>Nationalities or religious or political groups.</td>
  </tr>
  <tr>
    <td>ORDINAL</td>
    <td>Ordinal numbers like “first”, “second”.</td>
  </tr>
  <tr>
    <td>ORG</td>
    <td>Organizations (Companies, agencies, institutions, etc.)</td>
  </tr>
  <tr>
    <td>PERCENT</td>
    <td>Percentage values (including “%”).</td>
  </tr>
  <tr>
    <td>PERSON</td>
    <td>Person, including fictional characters.</td>
  </tr>
  <tr>
    <td>PRODUCT</td>
    <td>Commercial products (Vehicles, weapons, foods; excludes services).</td>
  </tr>
  <tr>
    <td>QUANTITY</td>
    <td>Measurements, as of weight or distance.</td>
  </tr>
  <tr>
    <td>TIME</td>
    <td>Sub-day time expressions.</td>
  </tr>
  <tr>
    <td>WORK_OF_ART</td>
    <td>Creative works.</td>
  </tr>
</table>

Table 8: Entity type and definition of the Ontonotes5 dataset.

<table>
  <tr>
    <th>Rate Method</th>
    <th>t=1</th>
    <th>t=2</th>
    <th>t=3</th>
    <th>t=4</th>
    <th>t=5</th>
    <th>Avg.</th>
  </tr>
  <tr>
    <td rowspan="2">10%</td>
    <td>DPI(Ours)</td>
    <td>82.85</td>
    <td>71.40</td>
    <td>56.95</td>
    <td>48.86</td>
    <td>42.76</td>
    <td>**60.56**</td>
  </tr>
  <tr>
    <td>RDP</td>
    <td>1.21</td>
    <td>0.04</td>
    <td>0.32</td>
    <td>0.28</td>
    <td>0.23</td>
    <td>0.42</td>
  </tr>
  <tr>
    <td rowspan="2">5%</td>
    <td>DPI(Ours)</td>
    <td>77.43</td>
    <td>65.28</td>
    <td>49.12</td>
    <td>40.65</td>
    <td>38.16</td>
    <td>**54.13**</td>
  </tr>
  <tr>
    <td>RDP</td>
    <td>1.21</td>
    <td>0.42</td>
    <td>0.31</td>
    <td>0.06</td>
    <td>0.15</td>
    <td>0.43</td>
  </tr>
</table>

Table 9: Performance in low-resource conditions on the I2B2 (Murphy et al., 2010) dataset under the FG-8-PG-2 INER setting.
Figure 9: Category (Entity type) F1 of the Ontonotes5(Hovy et al., 2006) dataset under the FG-8-PG-2 setting.

Reviewer paper summaries: This paper proposes a parameter-efficient dynamic prefix as a task instructor (DPI) method for sequence-to-sequence-based incremental named entity recognition. By integrating an adapter module and incorporating self-entropy and task-aware similarity losses, the approach minimizes parameter updates and mitigates catastrophic forgetting. Experimental results demonstrate the effectiveness of the proposed model.

This work introduces a dynamic prefix method and formalizes INER as a seq2seq generation task. By employing the dynamic prefix based on a seq2seq generation framework, our method retains task-invariant capabilities and preserves task-specific knowledge in INER.

This paper proposes a Dynamic Prefix as Instructor (DPI) method for Incremental Named Entity Recognition (INER), achieving both task-invariant capability retention and task-specific knowledge preservation with minimal trainable parameters. The DPI training incorporates three components: Label Augmentation Strategy, Self-entropy Loss, and Task-aware Similarity Loss. Experimental results under multiple settings demonstrate the superiority of the proposed method over baseline approaches.
Summary of strengths: - The proposed approach, which preserves parameters for existing entity types while adapting a small set for new types, effectively maintains current model outputs and represents a sound and novel contribution. 
- Experimental results validate the model's effectiveness, and the ablation study demonstrates the impact of each component. 
- The paper is well-structured and clearly written.

The paper introduces DPI, which effectively addresses the catastrophic forgetting problem in INER. By leveraging task-specific dynamic prefixes, the model efficiently retains previously learned knowledge while adapting to new entity types with minimal parameter updates

1. The adaptation of dynamic prefix-tuning to INER effectively addresses incremental learning challenges.
2. The introduced components (Label Augmentation Strategy, Self-entropy Loss, and Task-aware Similarity Loss) collectively enhance model performance, with ablation studies validating their individual contributions.
Summary of weaknesses: - The explanation of the loss functions lacks clarity and requires further elaboration. 
- A more detailed error analysis, including specific instances, would have provided valuable insights. 
- While the authors highlight the parameter efficiency, the doubling of parameters and the use of a sequence-to-sequence architecture raise questions about the model's training/inference time and memory cost compared to encoder-based models. 
- The paper does not mention the availability of the code.

1. My primary concern with this paper is that the comparative experimental setup is unfair. The proposed DPI method is based on the T5 model, while baselines such as RDP and CFNER are built upon BERT. Given the inherent differences in parameter scale and performance between T5-based and BERT-based models, the current comparison does not convincingly demonstrate the superiority of DPI. A more rigorous comparison should involve baselines that use comparable model architectures to ensure fairness.

2. I am uncertain whether it is necessary to model the INER task as a generative task using a seq2seq model. For the datasets used in this paper—CoNLL2003, I2B2, and OntoNotes5—the understanding-based approaches are already sufficient. As shown in Tables 1 and 2, DPI achieves only a 2% performance improvement over existing methods. Unless more complex INER datasets are introduced, I believe the increased complexity and computational cost associated with using a generative approach are not justified by the relatively marginal performance gains.

3. This paper lacks comprehensive citations and comparisons with related work:

[1] Continual named entity recognition without catastrophic forgetting. EMNLP 2023.

[2] Decomposing Logits Distillation for Incremental Named Entity Recognition. SIGIR 2023.

[3] SKD-NER: continual named entity recognition via span-based knowledge distillation with reinforcement learning. EMNLP 2023.

[4] Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models. ACL 2024.

[5] Flexible Weight Tuning and Weight Fusion Strategies for Continual Named Entity Recognition. ACL 2024.

[6] Incremental Sequence Labeling: A Tale of Two Shifts. ACL 2024.

1. The rationale for using Self-entropy Loss requires clarification. While it encourages more confident predictions, this confidence may not inherently align with correctness. The paper would benefit from:
    * Theoretical justification or empirical evidence explaining why Self-entropy Loss improves performance
    * Discussion of prerequisite conditions for its effectiveness (as performance gains likely depend on specific model architectures/scenarios)
2. The interplay between components merits deeper analysis:
    * Are the three strategies complementary or independent in their effects?
    * How do they interact with the dynamic prefix foundation? Are they specific to prefix-based methods or generalizable to other INER approaches? Such analysis would strengthen the understanding of performance improvements.
Comments, suggestions and typos: - See the weaknesses above.
- l in equation (10) and Sim in equation (13) is not defined.

N/A

None.
Overall assessment scores: 3

3

3
Metareview score: 3
Metareview: This paper introduces a parameter-efficient method, Dynamic Prefix as Instructor (DPI), to address the Incremental Named Entity Recognition (INER) task, which is a type of continual learning.
The work formalizes INER within a unified seq2seq generation framework and incorporates a dynamic prefix mechanism to balance task-invariant retention with adaptation to new entity types.
It proposes self-entropy loss, task-aware similarity loss, and a label augmentation strategy to address the stability-plasticity dilemma.

While reviewers raised valid concerns regarding the experimental design, particularly the fairness of comparisons with BERT-based baselines, the overall contribution is well-suited for the Findings.
Summary of reasons to publish: - The use of dynamic prefixes and adapter modules demonstrates high performance with minimal trainable parameters.
- The paper presents comprehensive experiments with ablation studies and reports consistent gains, especially in low-resource settings.