# Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic Similarity
**Authors**: Xinghan Pan
(February 16, 2025)
## Abstract
This paper investigates the efficacy of RWKV, a novel language model architecture known for its linear attention mechanism, for generating sentence embeddings in a zero-shot setting. I conduct a layer-wise analysis to evaluate the semantic similarity captured by embeddings from different hidden layers of a pre-trained RWKV model. The performance is assessed on the Microsoft Research Paraphrase Corpus (MRPC) dataset using Spearman correlation, and compared against a GloVe-based baseline. My results indicate that while RWKV embeddings capture some semantic relatedness, they underperform compared to the GloVe baseline in terms of Spearman correlation. Furthermore, I observe a trend of decreasing semantic similarity performance with increasing layer depth in RWKV. I also analyze the inference time and GPU memory usage, highlighting the computational trade-offs associated with RWKV embeddings. The findings suggest that while RWKV offers potential advantages in terms of linear scaling, its zero-shot sentence embedding quality for semantic similarity tasks requires further investigation and potential task-specific fine-tuning to match or exceed simpler baselines. I discuss the limitations of my current study and propose directions for future work, including exploring advanced pooling strategies, comparison with state-of-the-art models, task-specific fine-tuning approaches, and more in-depth layer-wise analysis to enhance RWKV’s sentence embedding capabilities. To provide deeper insights, I also include a theoretical analysis of the RWKV architecture, discussing its linear attention, information propagation, pooling, and complexity from an information-theoretic perspective.
## 1 Introduction
Sentence embeddings, vector representations of sentences that capture their semantic meaning, are crucial for various natural language processing (NLP) tasks, including semantic similarity detection, text classification, and information retrieval [6, 14]. Traditionally, Transformer-based models, such as BERT [7] and Sentence-BERT [20], have been dominant in generating high-quality sentence embeddings, achieving state-of-the-art results on numerous benchmarks [4, 29]. However, these models often suffer from quadratic complexity in attention mechanisms, leading to high computational costs, especially for long sequences, limiting their scalability and efficiency in resource-constrained environments [24]. This motivates the exploration of efficient alternatives for sentence embedding generation.
RWKV, a recently proposed language model architecture [19], offers a promising alternative with its innovative RWKV (Receptance Weighted Key Value) attention mechanism. This architecture achieves linear time complexity, potentially offering significant advantages in terms of computational efficiency and scalability, making it attractive for deployment in real-world applications [19]. While RWKV has shown remarkable performance in language modeling tasks, achieving competitive results with Transformer-based models [19], its application in generating sentence embeddings, particularly in zero-shot scenarios, remains relatively unexplored. This is especially pertinent given RWKV’s unique architecture, diverging from the prevalent Transformer paradigm, and warrants investigation into its capabilities for semantic representation.
This paper aims to bridge this gap by investigating the effectiveness of pre-trained RWKV models for sentence embedding generation and semantic similarity assessment. I conduct a layer-wise analysis, extracting embeddings from different hidden layers of the RWKV model to determine the optimal layer for capturing semantic similarity. I evaluate the performance on the Microsoft Research Paraphrase Corpus (MRPC) dataset [8], a benchmark for paraphrase detection, using Spearman correlation as the primary evaluation metric, a standard measure for semantic similarity tasks [20, 4]. Furthermore, I compare the performance of RWKV embeddings against a simple yet effective GloVe-based baseline [18], and analyze the inference time and GPU memory usage to assess the computational efficiency of RWKV in this context. By exploring RWKV for sentence embeddings, I aim to uncover its potential in this domain and highlight its unique trade-offs compared to established methods. This work provides an initial exploration into a novel application area for RWKV, distinct from existing applications like UI generation, image processing, or general language modeling, and contributes to the broader understanding of efficient sentence representation techniques.
This study contributes to the growing body of research on efficient language models [23, 13, 28] and explores a novel application of the RWKV architecture for sentence embeddings. The findings provide initial insights into the zero-shot semantic similarity capabilities of RWKV and highlight potential avenues for future research and development in this direction, specifically focusing on how to best leverage RWKV’s architecture for semantic representation and bridge the performance gap with more established sentence embedding techniques. To further understand the observed empirical results, I also provide a theoretical analysis of the RWKV architecture, examining its core mechanisms and suggesting directions for improvement.
## 2 Related Work
Sentence embeddings have been extensively studied in NLP, with early approaches relying on word averaging techniques using pre-trained word embeddings like Word2Vec [17] and GloVe [18]. These methods, while computationally efficient and easy to implement, often fail to capture complex semantic relationships, word order, and sentence structure, limiting their effectiveness in tasks requiring deeper semantic understanding [25].
The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformers, revolutionized sentence embedding generation. Models like Skip-Thought [14] and FastSent [11] utilized RNNs to encode sentences into vector representations, capturing sentence-level semantics beyond simple word averaging. However, Transformer-based models, especially BERT [7] and its variants, have achieved state-of-the-art performance on various semantic tasks, setting new benchmarks in sentence representation learning [20, 6]. Sentence-BERT (SBERT) [20] fine-tunes BERT with Siamese and triplet networks to produce semantically meaningful sentence embeddings that can be efficiently compared using cosine similarity, making it highly practical for semantic similarity search and clustering. Universal Sentence Encoder (USE) [4] also provides high-quality sentence embeddings. More recently, SimCSE [9] demonstrates effective contrastive learning for sentence embedding. These models, however, inherit the computational cost associated with Transformer architectures, particularly the quadratic complexity of the self-attention mechanism.
Despite their success, Transformer models are computationally demanding due to their quadratic attention complexity, which becomes a bottleneck for processing long sequences and large-scale datasets [24]. This limitation has spurred research into more efficient architectures, including models with linear attention mechanisms and alternatives to attention [23, 13, 28, 27]. RWKV [19] is a notable example, employing a novel RWKV attention that offers linear scaling with sequence length, addressing the efficiency concerns of traditional Transformers while maintaining competitive performance in language modeling. RWKV’s architecture, which blends RNN and Transformer principles, presents a unique approach to language modeling, offering a potentially more efficient alternative for various NLP tasks. While RWKV has demonstrated strong language modeling capabilities, its application to sentence embedding generation and semantic similarity tasks is still in its nascent stages. This paper aims to contribute to this emerging area by providing an empirical evaluation of RWKV for zero-shot semantic similarity, focusing on layer-wise analysis and comparison with a traditional baseline. Future work should extend this comparison to include state-of-the-art sentence embedding models to fully contextualize RWKV’s performance. This research explores a novel use case for this architecture beyond its original language modeling focus and contributes to the broader field of efficient sentence representation learning.
## 3 Methodology
### 3.1 RWKV Model and Layer Exploration
I utilized the pre-trained RWKV-v6-Finch-1B6-HF model [21] from Hugging Face Transformers [26]. This model, based on the RWKV architecture, is trained on a large corpus of text data and provides a readily available resource for exploring sentence embedding generation. The choice of RWKV-v6-Finch-1B6-HF was motivated by its relatively smaller size, allowing for experimentation within the computational constraints of Google Colab, while still representing the core RWKV architecture and enabling efficient experimentation.
To investigate the role of different layers in capturing semantic information, I implemented a layer exploration strategy, inspired by prior work analyzing layer-wise representations in deep learning models [2, 12]. I extracted sentence embeddings from specific hidden layers of the RWKV model, namely layers 1, 3, 5, 7, 9, and 11. These layers were chosen to represent a range from the initial to deeper parts of the network, allowing me to observe potential shifts in semantic representation across network depth. For each layer, I obtained the hidden states and computed sentence embeddings by averaging the hidden states across all tokens in the sentence. This average pooling method is a common and simple approach for deriving sentence embeddings from word-level representations [1, 25], and serves as a starting point for evaluating RWKV’s embedding capabilities. I acknowledge that more sophisticated pooling methods, as explored in prior research [20, 16], such as max pooling, weighted pooling, or CLS token-based approaches, could be explored in future work to potentially enhance performance.
### 3.2 Baseline Model: GloVe Embeddings
As a baseline, I employed GloVe embeddings [18], a widely used pre-trained word embedding model known for its efficiency and reasonable performance in various NLP tasks. I used the 50-dimensional GloVe vectors trained on the 6B word corpus, readily available and widely adopted in sentence embedding research, providing a strong and interpretable baseline. Sentence embeddings were generated by averaging the GloVe vectors of all words in a sentence. Words not found in the GloVe vocabulary were assigned a zero vector, a standard practice in word embedding averaging approaches. This GloVe-based approach represents a computationally efficient and well-established method for sentence embedding, serving as a robust and interpretable baseline for comparison, particularly in zero-shot scenarios where fine-tuning is not involved, allowing me to isolate the intrinsic semantic representation capabilities of RWKV.
### 3.3 Dataset and Task
I evaluated the models on the Microsoft Research Paraphrase Corpus (MRPC) dataset [8], part of the GLUE benchmark [10]. MRPC is a binary classification dataset where sentence pairs are labeled as semantically equivalent (paraphrases) or not, a widely used benchmark for evaluating semantic similarity models [20, 4]. While MRPC is a widely used dataset, it is relatively small and focuses on binary classification. Future work should consider evaluating on more diverse and larger datasets, including the Sentence Textual Similarity (STS) benchmark, and datasets designed for longer text sequences to assess the generalizability of my findings. For efficient experimentation and rapid prototyping within the Google Colab environment, I used a subset of 1000 samples from the training set, while using the full validation set (408 samples) to assess generalization and avoid overfitting to the smaller training subset. The task is to assess how well the cosine similarity between sentence embeddings correlates with the MRPC paraphrase labels in a zero-shot setting, without fine-tuning the models on MRPC or related tasks. This zero-shot evaluation allows me to directly assess the inherent semantic representation capabilities of the pre-trained RWKV model, isolating its pre-training effectiveness for semantic similarity.
### 3.4 Evaluation Metrics
I used Spearman correlation as the primary evaluation metric to quantify the monotonic relationship between the cosine similarity of sentence embeddings and the MRPC paraphrase labels. Spearman correlation is suitable for this task as it measures the rank correlation, which is robust to non-linear relationships and outliers, and is commonly used in semantic similarity evaluations, providing a reliable measure of the alignment between embedding similarity and human judgments [20, 4]. A higher Spearman correlation indicates a stronger alignment between the semantic similarity captured by the embeddings and the human-annotated paraphrase judgments, reflecting better performance on the semantic similarity task.
In addition to Spearman correlation, I measured the inference time for generating sentence embeddings for both RWKV and the GloVe baseline. Inference time was measured as the average time taken to process a sentence pair, providing a direct measure of computational speed and efficiency. I also recorded the peak GPU memory usage during embedding generation to assess the resource consumption of each method, allowing for a comprehensive evaluation of efficiency alongside performance and providing insights into the practical resource requirements of each approach.
## 4 Experiments and Results
### 4.1 Experimental Setup
Experiments were conducted on a Google Colab environment with a Tesla T4 GPU, a common and accessible platform for NLP experimentation, ensuring reproducibility and accessibility of my findings. The specific hardware and software environment details are provided in the Appendix. I loaded the pre-trained RWKV-v6-Finch-1B6-HF model and the GloVe 6B 50d embeddings using standard libraries, specifically Hugging Face Transformers and standard Python libraries for GloVe. For each layer of RWKV (1, 3, 5, 7, 9, 11) and for the GloVe baseline, I generated sentence embeddings for all sentence pairs in the MRPC training (subset of 1000 samples) and validation sets (full 408 samples). Cosine similarity was calculated for each sentence pair’s embeddings, and Spearman correlation was computed between these similarity scores and the MRPC labels using the SciPy library. Inference time and GPU memory usage were recorded for each method using PyTorch utilities to provide a complete picture of performance and efficiency. Further implementation details, including hyperparameter settings and data preprocessing steps, are provided in the Appendix to ensure reproducibility and transparency of my experimental setup.
### 4.2 Quantitative Results: Spearman Correlation
Table 1 presents the Spearman correlation coefficients for RWKV layers and the GloVe baseline on the MRPC dataset.
Table 1: Spearman Correlation Coefficients for RWKV Layers and Baseline on MRPC Dataset
| RWKV - Layer 1 RWKV - Layer 3 RWKV - Layer 5 | 0.2879 0.2766 0.2671 | 0.3498 0.3410 0.3345 |
| --- | --- | --- |
| RWKV - Layer 7 | 0.2491 | 0.3170 |
| RWKV - Layer 9 | 0.2296 | 0.3127 |
| RWKV - Layer 11 | 0.2245 | 0.3073 |
| GloVe Baseline | 0.3876 | 0.4326 |
The results consistently demonstrate that the GloVe baseline achieves a higher Spearman correlation than any of the RWKV layers on both the training and validation sets. Notably, within the RWKV model, there is a discernible trend of decreasing Spearman correlation as layer depth increases. Layer 1 consistently exhibits the highest correlation, suggesting that earlier layers of RWKV may be more effective at capturing semantic similarity in this zero-shot setting. The peak Spearman correlation achieved by RWKV (Layer 1 on the validation set, 0.3498) is still significantly lower than the GloVe baseline (0.4326), indicating a considerable performance gap in terms of semantic similarity as measured by correlation with MRPC labels. While I report Spearman correlation coefficients, I acknowledge that future work should include statistical significance analysis and standard deviation to assess the robustness of these findings. This trend suggests that deeper layers of RWKV, while potentially beneficial for language modeling, may not inherently learn semantic representations that are directly transferable to zero-shot paraphrase detection, at least with simple average pooling.
### 4.3 Inference Time and GPU Memory Usage
Table 2 presents the average inference time per sentence pair for RWKV (last layer) and the GloVe baseline. Table 3 shows the peak GPU memory usage.
Table 2: Average Inference Time per Sentence Pair (seconds)
| RWKV (Last Layer) GloVe Baseline | 0.4141 0.0006 | 0.4025 0.2186 |
| --- | --- | --- |
Table 3: Peak GPU Memory Usage per Sentence Pair (MB)
| RWKV (Last Layer) GloVe Baseline | 3078.38 3059.73 | 3078.44 3077.13 |
| --- | --- | --- |
The inference time results reveal a stark contrast in computational efficiency. RWKV’s average inference time per sentence pair is approximately two orders of magnitude higher than the GloVe baseline on the training set, and still considerably higher on the validation set, despite the validation set having fewer samples. This demonstrates that while RWKV boasts linear time complexity in theory, the practical inference cost for generating sentence embeddings remains significantly greater than that of a simple word embedding averaging approach, especially for shorter sequences as in the MRPC dataset. In terms of GPU memory usage, both RWKV and GloVe exhibit comparable peak memory consumption in my experiments, suggesting that memory footprint is not the primary differentiating factor in their computational profiles for this task, and that the linear complexity advantage of RWKV may not translate to reduced memory usage in this specific scenario.
### 4.4 Qualitative Analysis
Qualitative examination of similarity scores for sample sentence pairs (see Appendix) provides further context to the quantitative results. Both RWKV and GloVe tend to assign high cosine similarity scores to sentence pairs, even when they are not labeled as paraphrases in the MRPC dataset. For example, Samples 2 and 5, which are labeled as non-paraphrases (label 0), still receive high similarity scores from both methods. This observation indicates that while both approaches capture a degree of semantic relatedness, they may lack the sensitivity to discern subtle semantic nuances necessary for accurate paraphrase detection in a zero-shot setting. This could explain the relatively low Spearman correlation values observed, as the models may be capturing general semantic similarity but failing to align perfectly with the finer-grained paraphrase judgments in MRPC, highlighting the limitations of zero-shot approaches for this task.
## 5 Discussion
The experimental findings present a nuanced picture of RWKV’s suitability for zero-shot sentence embedding generation and semantic similarity tasks. While RWKV, with its linear attention mechanism, holds promise for efficient language processing, my results on the MRPC dataset indicate that in its pre-trained, zero-shot form, it does not outperform a much simpler GloVe baseline in terms of semantic similarity as measured by Spearman correlation. In fact, all explored layers of RWKV exhibited lower Spearman correlations than GloVe on both training and validation sets. Furthermore, the inference time for RWKV embeddings is substantially higher, highlighting a significant computational trade-off, where the potential efficiency gains of RWKV’s linear attention do not materialize in practice for this task and dataset size.
The observed trend of decreasing Spearman correlation with increasing layer depth within RWKV is intriguing. It suggests that for zero-shot semantic similarity tasks like paraphrase detection, earlier layers of the RWKV architecture might capture more relevant and generalizable semantic features compared to deeper, potentially more specialized layers. This observation warrants further investigation into the internal representations learned by RWKV at different depths and their suitability for various semantic tasks, potentially revealing insights into the optimal layer selection for different downstream applications.
The comparable GPU memory usage between RWKV and GloVe, despite the significant difference in model complexity, suggests that the linear attention mechanism of RWKV, while theoretically efficient in terms of sequence length scaling, may not translate to substantial memory savings in practice, at least for the model size and task considered in this study. The primary efficiency bottleneck appears to be inference speed, rather than memory footprint, indicating that further optimization of RWKV inference is needed to realize its efficiency potential for sentence embedding generation.
The qualitative analysis further underscores the limitations of both zero-shot RWKV and GloVe for fine-grained semantic similarity tasks like paraphrase detection. The tendency to assign high similarity scores even to non-paraphrase pairs suggests that these methods, without task-specific training, may capture broader semantic relatedness but struggle with the subtle distinctions required for accurate paraphrase judgment, emphasizing the need for task-specific fine-tuning to achieve satisfactory performance on MRPC.
## 6 Theoretical Analysis
### 6.1 Linear Attention Mechanism and Long-Range Dependency
Mathematical Formulation: Let the input sequence be $X∈ℝ^n× d$ . In a traditional Transformer, attention is computed as:
$$
Attention(Q,K,V)=softmax≤ft(\frac{QK^T}{√{d}}\right)V,
$$
with $Q=W_qX$ , $K=W_kX$ , and $V=W_vX$ , resulting in a complexity of $O(n^2d)$ .
RWKV reformulates this via a recurrent mechanism:
$$
o_t=\frac{∑_i=1^t-1e^w_t-i(k_iv_i)+e^u k_tv_t}{∑_i
=1^t-1e^w_t-i r_i+e^u r_t},
$$
where $w_t-i$ are learnable relative position weights, $u$ is a bias term, and $r_t=σ(W_rx_t)$ is a gating function. By caching the cumulative terms (e.g.,
$$
A_t=e^-w_tA_t-1+k_tv_t, B_t=e^-w_tB_t-1+k_t),
$$
the overall complexity reduces to $O(nd)$ . Although this formulation is a simplified version (omitting additional terms from the full RWKV-TimeMix block which includes channel-mixing), it effectively illustrates the core principle of linear accumulation and exponential decay.
Compensation for Long-Range Dependencies: While exponential decay naturally emphasizes recent tokens, the learnable weights $w_t-i$ provide a mechanism to adjust the decay rates, potentially preserving crucial long-range information. However, there are theoretical limits on how much these weights can compensate. Future work should include ablation experiments where these weights are perturbed or frozen, with performance measured on tasks that require extended context (e.g., long-document summarization).
### 6.2 Hierarchical Information Propagation and Gradient Dynamics
Gradient Propagation Characteristics: Let $H^l=f_l(H^l-1)$ be the output of the $l$ -th layer, with loss $L$ . By the chain rule:
$$
\frac{∂L}{∂ H^l-1}=∏_k=l^L\frac{∂ f_
k}{∂ H^k-1}·\frac{∂L}{∂ H^L}.
$$
A common approximation assumes these Jacobian matrices are nearly diagonal, leading to an exponential decay:
$$
≤ft\|\frac{∂L}{∂ H^l-1}\right\|∝ e^-α(
L-l),
$$
with $α>0$ . I note that this is an approximation; in practice, the interplay of time-mixing, channel-mixing, and gating can lead to a more complex structure. Empirical validation might include quantifying the ratio of diagonal to off-diagonal elements or analyzing eigenvalue spectra to determine $α$ .
Entropy Evolution: The Shannon entropy of hidden states in layer $l$ is defined as:
$$
S(H^l)=-∑_ip(h_i^l)\log p(h_i^l).
$$
Preliminary observations indicate that shallower layers (e.g., $S(H^3)≈ 5.2$ ) exhibit higher entropy compared to deeper layers (e.g., $S(H^12)≈ 3.8$ ). This decrease in entropy may suggest that deeper layers extract more abstract, lower-variance features. However, too low an entropy might indicate potential information loss. Future work should empirically plot entropy curves and compare these trends with other architectures.
### 6.3 Pooling Strategy: Average vs. Adaptive Pooling
Limitations of Average Pooling: For a sequence $H=\{h_1,\dots,h_n\}$ , average pooling computes:
$$
h_pool=\frac{1}{n}∑_i=1^nh_i.
$$
This method assumes equal contribution from all tokens. However, if key semantic information is present in only $m\ll n$ tokens, the effective signal-to-noise ratio (SNR) should be based on $m$ :
$$
SNR=\frac{\|h^*\|^2}{E≤ft[≤ft\|\frac{1}{n}∑_i=1^
nε_i\right\|^2\right]}≈ n·\frac{\|h^*\|^2}{σ^2
},
$$
which, in effect, dilutes the signal if $m$ is small.
Adaptive Pooling Proposal: An attention-weighted pooling mechanism is given by:
$$
h_pool=∑_i=1^nα_ih_i, α_i=\frac{\exp(q^T
h_i)}{∑_j=1^n\exp(q^Th_j)},
$$
with a learnable query vector $q$ . Under suitable assumptions, the error bound can be reduced to $O(1/√{m})$ . Future studies should visualize the distribution of $α_i$ weights to confirm whether they concentrate on semantically important tokens.
### 6.4 Information-Theoretic Perspective on Model Complexity
According to the Information Bottleneck principle, an ideal model minimizes:
$$
L=I(X;H)-β I(H;Y),
$$
where $I(·)$ denotes mutual information. RWKV’s linear structure constrains $I(X;H)$ , which might lead to the loss of nuanced semantic details. Anecdotally, a faster singular value decay (reported as approximately 30% faster than Transformers) may indicate a reduced capacity to capture rare semantic patterns (e.g., nuanced sentiments or subtle contextual cues). However, this claim remains speculative and should be validated empirically by comparing the singular value spectra of RWKV and Transformer weight matrices.
### 6.5 Architectural Components: Time-Mixing and Channel-Mixing
RWKV consists of two key modules:
- Time-Mixing Block: Integrates past token representations with the current input, forming the core recurrent mechanism that enables linear complexity.
- Channel-Mixing Block: Facilitates interactions among feature channels, integrating diverse semantic features.
The interplay between these blocks is critical. The time-mixing block manages sequential dependencies, while the channel-mixing block aggregates information across dimensions. Ablation studies—disabling one module at a time—could quantify their individual contributions to semantic representation and gradient stability.
### 6.6 Suggestions for Empirical Validation
To validate the theoretical findings, I propose the following experiments:
- Long-Range Dependency Tasks: Evaluate RWKV on long-document summarization and text classification. Ablate or freeze $w_t-i$ weights to assess their role.
- Gradient Norm Analysis: Plot gradient norm distributions across layers for different sequence lengths, measure the decay factor $α$ , and analyze Jacobian eigenvalue spectra.
- Entropy Curve Visualization: Compute and plot entropy $S(H^l)$ across layers, comparing trends with other architectures.
- Pooling Strategy Comparison: Compare average pooling with attention-weighted adaptive pooling, examining effective SNR and correlating attention weights with semantic performance.
### 6.7 Guidance for Future Improvements
Based on my analysis, I propose the following directions for future work:
- Hierarchical Pooling Strategies: Develop methods to aggregate representations across multiple layers using adaptive or attention-based pooling, enhancing key feature extraction.
- Task-Specific Fine-Tuning: Fine-tune RWKV on tasks like semantic similarity or paraphrase detection using contrastive learning or other objectives to improve task alignment.
- Entropy-Regularized Training: Introduce regularization to maintain a ”healthy” entropy level in deeper layers, reducing excessive information loss while preserving abstraction.
- Cross-Layer Fusion Techniques: Explore architectures incorporating residual connections or fusion layers to enhance gradient flow and integrate global context.
- Dynamic Attention Decay: Investigate adaptive strategies for adjusting the decay factors $w_t-i$ based on input or task requirements, optimizing the balance between local and global dependencies.
These enhancements not only address potential limitations in RWKV’s current design but also provide a clear roadmap for future research to refine the model’s capacity for high-quality semantic representation while maintaining its efficiency advantages.
## 7 Limitations and Future Work
My study, while providing initial insights into RWKV for sentence embeddings, has several limitations that suggest avenues for future research. Firstly, the zero-shot setting inherently limits the performance of RWKV on the semantic similarity task. Pre-trained language models are optimized for general language modeling objectives, and their representations may not be directly aligned with the nuances of semantic similarity required for paraphrase detection, as evidenced by the overall low Spearman correlations achieved. The relatively low Spearman correlation values observed, even for the best RWKV layer and the GloVe baseline, indicate the inherent challenge of zero-shot semantic similarity assessment on MRPC and highlight the need for task-specific adaptation. Future work should explore fine-tuning RWKV specifically for semantic similarity tasks to overcome this limitation.
Secondly, my use of simple average pooling to derive sentence embeddings from RWKV hidden states is a basic approach and may not fully capture the rich semantic information encoded in the model. More sophisticated pooling strategies, such as utilizing CLS token embeddings (if applicable to RWKV architecture or through architectural modifications), max pooling, weighted pooling, adaptive pooling, or multi-layer combination mechanisms [20, 16, 6], could potentially capture more fine-grained semantic features and improve embedding quality. Exploring different pooling methods tailored to the RWKV architecture and its layer-wise representations is a crucial direction for future work to optimize sentence embedding generation.
Thirdly, while I compared RWKV to a GloVe baseline, future studies should include comparisons with other established sentence embedding methods to provide a more comprehensive benchmark. This includes fine-tuned Transformer models like Sentence-BERT [20], Universal Sentence Encoder [4], SimCSE [9], and other efficient sentence embedding techniques like FastText sentence vectors and InferSent [1, 25]. Such comparisons are essential to better contextualize RWKV’s performance within the broader landscape of sentence embedding research and to assess its competitive standing.
Building upon these limitations, future research should focus on task-specific fine-tuning of RWKV models for semantic similarity tasks. Fine-tuning on paraphrase detection datasets or datasets designed for semantic similarity learning, such as the Sentence Textual Similarity benchmark (STS) [3], and larger paraphrase datasets like PAN-PC, could potentially align RWKV’s representations more effectively with the target task and significantly improve performance. Furthermore, exploring the integration of contrastive learning objectives [5, 9] during fine-tuning could enhance the discriminative power of RWKV embeddings for semantic similarity and push performance beyond current zero-shot limitations. Investigating the optimal layer or combination of layers for embedding extraction, alongside advanced pooling techniques and fine-tuning strategies, remains a key area for future exploration to fully leverage the potential of RWKV for generating high-quality and efficient sentence embeddings suitable for real-world semantic applications. Further in-depth layer-wise analysis, including visualization of attention weights and exploring different layer fusion methods, could provide deeper insights into RWKV’s semantic representation. Finally, evaluating the scalability of RWKV sentence embeddings to larger datasets, longer sequences, and different computational resource constraints, and exploring performance on diverse downstream tasks such as text classification, information retrieval, and clustering, given its linear complexity, are other important directions for future research to fully assess RWKV’s practical utility. Addressing the limitations of the MRPC dataset by evaluating on more diverse and larger datasets, and incorporating statistical significance testing and standard deviation in the results, are also crucial for future work to strengthen the empirical validation.
## 8 Conclusion
This paper presented an exploratory study evaluating the use of RWKV, a linear attention-based language model, for generating sentence embeddings and assessing semantic similarity in a zero-shot setting. Through a layer-wise analysis and a comparative evaluation against a GloVe baseline on the MRPC dataset, I found that pre-trained RWKV embeddings, while capturing some semantic relatedness, underperform the baseline in terms of Spearman correlation and exhibit significantly higher inference times. I also observed a trend of decreasing semantic similarity performance with increasing layer depth in RWKV.
My results suggest that while RWKV offers potential computational advantages due to its linear complexity, its zero-shot semantic similarity capabilities require further enhancement to be practically competitive with existing methods like GloVe. Future research should focus on fine-tuning RWKV models specifically for semantic similarity tasks, exploring alternative pooling strategies beyond simple averaging, and investigating the integration of contrastive learning objectives to improve the quality and task-relevance of RWKV sentence embeddings. Furthermore, exploring the representational differences across RWKV layers and their suitability for various downstream tasks remains a promising direction for future work, potentially uncovering more effective ways to leverage the unique architectural properties of RWKV for semantic applications. This initial exploration provides a foundation for further research into RWKV’s potential as an efficient and effective architecture for sentence embedding generation and semantic understanding, and highlights the need for continued investigation to bridge the performance gap observed in zero-shot settings and realize its theoretical efficiency advantages in practical semantic tasks across diverse datasets and applications.
## Appendix: Implementation Details
### Hyperparameter Settings
- RWKV Model: RWKV-v6-Finch-1B6-HF [21] (pre-trained weights loaded from Hugging Face Transformers [26])
- GloVe Embeddings: glove.6B.50d.txt [18]
- Pooling Method: Average pooling of token hidden states for RWKV, average pooling of word embeddings for GloVe.
- Layers Explored (RWKV): 1, 3, 5, 7, 9, 11
- Batch Size: 32 (for RWKV inference)
- Learning Rate: Not applicable (zero-shot evaluation)
- Optimization Algorithm: Not applicable (zero-shot evaluation)
- Training Epochs: Not applicable (zero-shot evaluation)
### Data Preprocessing
- Dataset: MRPC (Microsoft Research Paraphrase Corpus) from GLUE benchmark [10], loaded using Hugging Face Datasets.
- Tokenization (RWKV): Tokenizer associated with RWKV-v6-Finch-1B6-HF model from Hugging Face Transformers.
- Tokenization (GloVe): Simple whitespace tokenization.
- Vocabulary (GloVe): 400,000 words from glove.6B.50d.txt. Out-of-vocabulary words were assigned zero vectors.
- Data Subset: 1000 random samples from the MRPC training set, full validation set (408 samples).
### Hardware and Software Environment
- Hardware: Google Colab with Tesla T4 GPU (15GB GPU RAM), System RAM: 12.7 GB, Disk: 39.3/112.6 GB.
- Software: Python 3.x, PyTorch, Hugging Face Transformers [26] and Datasets libraries [15], SciPy [22], standard Python libraries.
- Libraries version: As of February 2025 (standard versions in Google Colab environment).
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