Soft Knowledge Prompt: Help External Knowledge Become a Better Teacher to Instruct LLM in Knowledge-based VQA (2024)

Abstract: LLM has achieved impressive performance on multi-modal tasks, which have received everincreasing research attention. Recent research focuses on improving prediction performance and reliability (e.g., addressing the hallucination problem). They often prepend relevant external knowledge to the input text as an extra prompt. However, these methods would be affected by the noise in the knowledge and the context length limitation of LLM. In our work, we focus on making better use of external knowledge and propose a method to actively extract valuable information in the knowledge to produce the latent vector as a soft prompt, which is then fused with the image embedding to form a knowledge-enhanced context to instruct LLM. The experimental results on knowledge-based VQA benchmarks show that the proposed method enjoys better utilization of external knowledge and helps the model achieve better performance.

heading: 1 Introduction
text: Although LLM-based methods already perform well on many multi-modal tasks, knowledge-based VQA remains a challenging task, which requires outside knowledge beyond the image content to answer the question. In practice, LLM often generates factually incorrect responses to the given questions since the knowledge in the LLM model may be inaccurate, incomplete, and outdated. Intuitively, a straightforward solution is to incorporate relevant external knowledge to assist LLM in making a better prediction. In nature, such a paradigm is equivalent to using external knowledge as an additional prompt for LLM.
Related research efforts mainly focus on training a better retriever to obtain relevant external knowledge (Lin and Byrne, 2022), but the model passively receives the retrieved result. In fact, even the
*Jing Liu is the corresponding author.
best retriever hardly guarantees that the retrieved results are without distracting information. Firstly, it is common that the retrieved knowledge might be irrelevant or part-irrelevant to answer the given question. Secondly, even if the retrieved knowledge is useful, LLMs often attend to the irrelevant parts or might completely ignore them, leading to generate the answer based on their incorrect knowledge. Therefore, it is non-trivial to study how to better utilize the retrieved knowledge.
As shown in Figure 1, while containing supporting content, the external knowledge snippet also contains distracting content. The precisely supporting content renders a solid foundation for a model to generate the correct output. Various attempts have been made to filter retrieved snippets based on rules (Wang et al., 2023), which focus largely on the NLP tasks and neglect to excavate supporting information in a fine-grained manner. Currently, existing models fail to pay sufficient attention to the supporting content and are prone to be distracted by surrounding sentences that share similar topics (Shi et al., 2023).
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Multi-modal LLM (MLLM) has achieved tremendous success in multi-modal tasks. It leverages a projector to transform images into latent vectors that provide visual information context to the LLM. Given relevant external knowledge, prior approaches often directly prepend it with textual input to prompt LLM. In fact, the textual knowledge snippets can be analogously transformed into latent vectors to prompt LLM. Such behavior offers the following advantages: 1) realize the finegrained extraction of pivotal information derived from external knowledge, facilitating the injection of knowledge into the prediction. 2) refine longer external knowledge texts into a small number of vectors, effectively avoiding adding numerous extra input tokens to LLM, which has a context length limitation.
Inspired by the empirical evidence mentioned above, we propose a novel method (coined SKP) to transform the external knowledge snippet into the latent vector as a Soft Knowledge Prompt to better instruct LLM for the knowledge-based VQA task. Specifically, we extract the pivotal information in the external knowledge according to the visual and question information and transform it into latent vectors, where the attention mechanism is introduced to select the informative tokens derived from the external knowledge. Then, knowledge latent vectors are fused with the visual embedding to form a knowledge-enhanced context for the question. Furthermore, we propose a tailored training scheme to expedite the process of selecting and fusing pivotal knowledge. For implementation details, please refer to the code at https: //github.com/BUAAw-ML/SKP.git. The experimental results verify that the proposed method enjoys better utilization of external knowledge and helps the model achieve better performance. Our main contribution can be summarized into threefold:
• Different from other works, we dig into the solution for extracting and utilizing pivotal information from relevant knowledge to better instruct LLM, which can suppress distracting information and elicits knowledge into output.
• Differing from prepending external knowledge to the input, we embed the knowledge into a soft prompt and fuse it with the visual embedding, effectively avoiding adding extra input tokens to LLM.
• Experimental results on outside-knowledge based VQA datasets demonstrate that the proposed method achieves promising performance, validating the effectiveness of key design choices.

heading: 2 Related Work

heading: 2.1 Prompt LLM for Knowledge-based VQA
text: A huge amount of implicit knowledge is embedded in the parameters of pre-trained models, which are endowed with powerful knowledge capabilities when given proper prompts. Yang et al. (Yang et al., 2022) take image caption as a hard prompt to instruct GPT-3 for knowledge-based VQA. However, the generated captions fail to cover all the necessary information in an image. Some works utilize additional information to prompt models. For example, Rubin et al. (Rubin et al., 2022) retrieve related training examples as prompts for in-context learning of LLM. Xenos et al. (Xenos et al., 2023a) leverage question-informative captions and informative examples to prompt LLaMA. Shao et al. (Shao et al., 2023) further induce GPT-3 based on numerous candidate answers. Unlike hard prompts, Dai et al. (Dai et al.) project visual features into latent vectors to help small-scale LLMs perform better on knowledge-based VQA tasks, verifying the potential of soft prompts.

heading: 2.2 Retrieve Knowledge for Knowledge-based VQA
text: As the literature claims, external knowledge benefits answering questions when incorporated into visual-language models (Gardères et al., 2020; Zheng et al., 2021). Recently, retrieving textual knowledge snippets can help to achieve a better performance (Qu et al., 2021; Gao et al., 2022). Concretely, Luo et al. (Luo et al., 2021) retrieve knowledge in a cross-modal way. Gui et al. (Gui et al., 2022) construct a knowledge retriever on top of GPT-3. Lin and Byrne (Lin and Byrne, 2022) jointly optimizes T5 and retriever to excavate informative external knowledge. Lin et al. (Lin et al., 2023a) retrieve external knowledge by a fine-grained and multi-modal approach. Moreover, some research efforts construct knowledge embeddings for retrieval (Hu et al., 2023), which would incur and accumulate additional errors. Nevertheless, these methods vastly regard “Pseudo Relevance Labels” (Lin and Byrne, 2022) as training signals for retriever, which fail to retrieve sufficient pivotal
knowledge for a model to make correct prediction (Chen et al., 2022; Wang et al., 2024).

heading: 2.3 Improve External Knowledge Utilization
text: Recent research attempts, such as (Luo et al., 2023; Lin et al., 2023b), focus on better exploiting retrieved information based on instruction tuning LLM in the NLP domain. However, the requirements for LLM tuning hinder the wide application of such methods. Another research line focuses more on selecting useful external knowledge for LLM in the NLP domain. For instance, Ram et al. (Ram et al., 2023) train a reranker to select more informative external knowledge. Li et al. (Li et al., 2023) propose a verifiable generation method where LLM updates the retrieval result until meeting the supporting fact that the retrieved documents can benefit answer to the question. Baek et al. (Baek et al., 2023) fine-tune a small flan model to judge whether the retrieved knowledge is useful or not and recalibrate the knowledge engagement in the output. Some works select external knowledge to enhance the result generated by the model (Peng et al., 2023; Zhang et al., 2023). These works focus on selecting valuable knowledge from multiple external knowledge candidates. Differently, our method not only selects valuable knowledge but also makes more efforts to better extract the pivotal information from external knowledge.
Recently, Wang et al. (Wang et al., 2023) select valuable sentences from the knowledge fragment by matching or entropy mechanism without guaranteeing that the selected sentences contribute to making correct predictions. This work employs a method of filtering sentences that is coarse-grained, and it needs to train a filter, which does not guarantee ease of use and generalization. In addition, this work focuses on NLP tasks rather than multi-modal tasks. Existing methods struggle to extract pivotal knowledge in a fine-grained and accurate manner, especially in the multi-modal domain, rendering an open problem for the research community. Our attention-based method can help to mine the valuable information in a more fine-grained manner.

heading: 3 Method
text: In the MLLM framework, a projector module is often used to transform the image content into visual context vectors for LLM. We focus on extracting pivotal information from relevant external knowledge and fusing it with the visual context vectors
as a knowledge-enhanced context to prompt LLM. The framework of the proposed method is illustrated in Figure 2. Specifically, the input is well processed firstly (Sec. 3.1), and we propose a method to mine valuable information from relevant external knowledge (Sec. 3.2) and fuse it with the visual vectors to instruct LLM (Sec. 3.3). Further, we adopt an ensemble-based approach to determine the final output (Sec. 3.4). Lastly, we design a tailored optimization scheme for the trainable components involved in our framework (Sec. 3.5).

heading: 3.1 Input Representations
text: In our framework, there are three types of inputs: question, image, and relevant external knowledge.
Question embedding. For the input question text, we use the embedding function of LLM to obtain the question embedding vectors: q ∈ Rn×d, in which n is the total number of tokens, and d represents the embedded dimension.
Image embedding. For an image, we adopt ViT (Fang et al., 2023) to encode the image into visual embeddings. Further, following the common practice, the Q-Former module and the linear layer are used to transform visual embeddings into the latent space of LLM, in which t query tokens are implemented to extract visual information most relevant to the question. The output query representation serves as the visual representation, denoted as V ∈ Rt×d, which includes the visual information of interest to the question.
Knowledge embedding. In practice, external knowledge is often fed to the model as a text snippet. Therefore, we leverage the embedding function of LLM to obtain the knowledge snippet embedding S ∈ Rl×d, in which l is the token number of knowledge snippet text.

heading: 3.2 Knowledge Mining
text: This section proposes a method to extract pivotal knowledge information from relevant external knowledge. Firstly, our method selects top-k most valuable knowledge snippets from k ′ relevant external knowledge snippets (k ≤ k′). To obtain k ′ knowledge snippets per data sample, a public retriever Vector Index Retriever based on LlamaIndex (Liu, 2022) can be used. Then, regarding each selected knowledge, we extract the valuable information to produce the latent vector as a soft knowledge prompt.
Specifically, we use the visual representation V , which contains the visual information of interest
to the question, to help select valuable information in the external knowledge snippet. Firstly, we calculate the following value as a query for selecting valuable information:
v = 1
t
t∑
i=1
Vi (1)
where v ∈ Rd. Because all the embeddings are in the latent space of LLM, we can directly measure the similarity between v and each token embedding Si(i ∈ [0, l]) of the external knowledge snippet. To capture more detailed relations, we calculate the multi-head similarity between v and Si. Specifically, v and Si are firstly multiplied with the parameter matrices W1 and W2 respectively:
v ′ = W1v S ′ i = W2Si
(2)
where W1 ∈ Rd×d and W2 ∈ Rd×d. Then, for the two projected vectors v ′ and S
′ i , the last dimension
of the vectors is split from (..., d) to (..., h, d/h). Thus, we can transform each vector into h vectors:
v ′ → {v′1, . . . , v ′ h} S ′ i → {S ′ i1, . . . , S ′ ih}
(3)
where h is the number of heads. Note that we set its value equal to the number of query tokens t (h = t). Lastly, for each pair (e.g. v
′ 1 and S ′ i1), we calculate
the dot product similarity ϕdot between them, and we can obtain h similarity values:
{ϕdot(v′1, S ′ i1), ..., ϕ dot(v ′ h, S ′ ih)} (4)
For l tokens in an external knowledge snippet, we calculate the multi-head similarity of each token according to the above process individually. Therefore, the dimension of the similarity value of an external knowledge is l×h. And we denote this similarity value as R ∈ Rl×h. Further, to obtain a score representing the external knowledge similarity, we take the mean of the similarity values of the l tokens as the final similarity score of an external knowledge text, which can be denoted as the following equation:
r = 1
l
l∑
i
[ 1
h
h∑
j
(Rij)] (5)
Based on the similarity score r, we select top-k knowledge snippets from k ′ knowledge snippets.
After acquiring the selected knowledge snippet, we propose an attention-based method to extract pivotal information and form a knowledge representation. Specifically, based on the relevance matrix R ∈ Rl×h, we calculate the relevance score of each token for h heads respectively, where the SoftMax function is introduced to obtain the normalized relevance score for each head R
′ j ∈ Rl(j ∈ [1, h]).
Then, according to the attention-based mechanism, we use the normalized relevance score R
′ j as the
weight vector and compute the weighted sum of the knowledge tokens through the following equation:
Pj = 1
l
l∑
i=1
R ′ jiSi (6)
where Pj ∈ Rd(j ∈ [1, h]). Last, the knowledge representations of all heads are concatenated together as a soft knowledge prompt:
P = [P1; ...;Ph] (7)
where [.; .] denotes the concatenation operation, and the soft knowledge prompt is denoted as P ∈ Rh×d.

heading: 3.3 Dynamic Fusion
text: The assumption hardly holds that the retriever always acquires useful knowledge. Besides, the importance of images and external knowledge for answering the question stochastically fluctuates for various samples. Therefore, after obtaining the soft knowledge prompt P , we introduce an adaptive fusion mechanism to dynamically fuse P with the visual representation V to obtain a knowledgeenhanced context to better instruct LLM.
To balance the proportion of external knowledge information and visual information, we concatenate V and P to predict scalars for fusion. Specifically, we use a gate-like structure with a Softmax function to dynamically calculate the weight value:
[σ1, σ2] = Softmax([FFN1(V); FFN2(P)]) (8)
where σ1 ∈ Rt×1 and σ2 ∈ Rh×1. Note that t = h. Then, based on the fusion weights, we employ the weighted sum of V and P to generate the knowledge-enhanced context C ∈ Rt×d:
C = σ1V + σ2P (9)

heading: 3.4 Answer Generation
text: Conditioned on k relevant external knowledge snippets, we can obtain k knowledge-enhanced contexts. Then, we concatenate each knowledgeenhanced context with the input question text and feed it into LLM to produce an answer output o. Then, majority voting is performed over the k output answers to determine the answer res:
res = argmax (count(o1, . . . , ok)) (10)
where the function count() records occurrences of each answer in the k outputs.

heading: 3.5 Optimization
text: In our design, the visual encoder and LLM parameters are frozen, and other parameters are trained to help better excavate pivotal information from external knowledge. Hence, the optimization scheme should encourage the occurrence of knowledgeenhanced contexts that are truly useful for prediction, and avoid knowledge-enhanced contexts without any contribution to the model’s prediction.
Firstly, we need to identify which knowledgeenhanced contexts are truly useful. Intelligibly, the positive knowledge-enhanced context should help the model make correct predictions when the model predicts incorrectly without using the knowledge. To achieve this goal, the input using only the image and question is fed into LLM and the output is denoted as ok+1. Then, a knowledge-enhanced context can be judged as positive when the LLM output using this knowledge-enhanced context is true and the output ok+1 is wrong. In summary, we identify three subsets of the knowledge-enhanced context based on whether they can correct the answer:
RP = {i ∈ [1, k] : oi = o∗ ∧ ok+1 ̸= o∗} RN = {i ∈ [1, k] : oi ̸= o∗ ∧ ok+1 = o∗} RI = {i ∈ [1, k] : i /∈ RP ∧ i /∈ RN} (11)
where o∗ denotes the ground truth answer, RP denotes the set including the positive knowledgeenhanced context, and RN denotes the set covering the negative knowledge-enhanced context that confuses answer generation. Furthermore, we denote RI as the set that belongs to neither RP nor RN , which is absent from calculating loss during training. Here our goal is to decrease the similarity scores of snippets in RN , and increase the similarity scores of snippets in RP . Mathematically, the model optimization is constricted by the following loss:
− ∑ (x,o∗)∈TrainSet [ ∑ i logp(o∗|x,Ci)
+ ∑
i∈RP logp(Ci|x)−
∑
i∈RN logp(Ci|x)]
(12) where x denotes the input sample. The first term
improves the answer generation performance. The second and last terms affect the external knowledge utilizing.

heading: 4 Experiments

heading: 4.1 Datasets
text: OK-VQA. The OK-VQA dataset (Marino et al., 2019) is a widely-used dataset for the outsideknowledge based VQA task, evaluating related knowledge-enhanced models. The questions in this dataset are crowd-sourced from Amazon Mechanical Turkers (AMT) 1. The dataset consists of 14,055 questions, of which 9, 009 questions are held out for training and 5, 046 questions are held out for testing. In addition, GS knowledge corpus (Luo et al., 2021) is used by the retriever to obtain the relevant knowledge in our experiments, which is a benchmark knowledge source for OK-VQA.
A-OKVQA. The A-OKVQA dataset (Schwenk et al., 2022) is a crowd-sourced dataset, which requires a wide range of world and commonsense knowledge to answer the questions. This dataset contains about 25K questions, in which 17K questions are used for training, and each sample on the training and validation sets is supplied with three relevant external knowledge snippets.

heading: 4.2 Experimental Setup
text: To thoroughly compare our results against existing methods, various evaluation metrics are adopted to measure the model performance. Specifically, VQA Score is the most used metric in VQA task, which is calculated with pre-processed human annotations A:
V QAScore(y,A) = min( #A(y)
3 , 1) (13)
in which #A(y) denotes the number of annotators who answered y.
Exact Match (EM) is also adopted as the evaluation metric in our work, which treats annotated answers equally:
1https://www.mturk.com
EM(y,A) = min(#A(y), 1) (14)

heading: 4.3 Implementation Details
text: We optimize the proposed model in the Pytorch framework with AdamW optimizer, a batch size of 1, gradient accumulation step of 8, and we use NVIDIA A6000 GPUs. The initial learning rate is set to 1e-5, and the weight decay is set to 0.05; the warm-up learning rate and steps are set to 1e8 and 1000, respectively. In our method, t is set to 32, and h is also set to 32. In addition, k ′ is set to 10 in experiments of OK-VQA and set to 3 in experiments of A-OKVQA. The experiment of each method is repeated 5 times, and the final result is calculated via the average over the 5 runs.

heading: 4.4 Comparison Methods
text: On the OK-VQA dataset, we compare the proposed method with a wide range of public approaches. Wherein, ConceptBERT (Gardères et al., 2020), KRISP (Marino et al., 2021), UnifER (Guo et al., 2022), VRR (Luo et al., 2021), KAT-T5 (Gui et al., 2022), TRiG-Ensemble (Gao et al., 2022), RA-VQA (Lin and Byrne, 2022), MAVEx (Wu et al., 2022), RA-VQA-v2 (Lin et al., 2023a), VLC-BERT (Ravi et al., 2023) and TRiG (Ensemble) (Gao et al., 2022) are relatively small in model size (<10B), MM-Reasoner (Khademi et al., 2023), PICa (Yang et al., 2022), KAT (Gui et al., 2022), Prophet (Shao et al., 2023), PromptCap (Hu et al., 2022), REVIVE (Lin et al., 2022), Flamingo (Alayrac et al., 2022), and TwO (Si et al., 2023) use very large models such as GPT-3 (175B). Note that (Sun et al., 2023) and (Xenos et al., 2023b) do not offer the name of the proposed method.
On the A-OKVQA dataset, we compare with various methods, including ViLBERT (Lu et al., 2019), LXMERT (Tan and Bansal, 2019), ClipCap (Mokady et al., 2021), KRISP, GPV-2 (Kamath et al., 2022), Prophet (Shao et al., 2023).

heading: 4.5 Main Results
text: Table 1 reports the comparison results on the OKVQA dataset. One can figure out the following observations. Firstly, compared with the methods (e.g., KAT-T5, RA-VQA) retrieving textual knowledge snippets, knowledge graph-based methods (e.g., ConceptBERT, KRISP) perform less satisfactorily. The reason may be that knowledge graphbased methods fail to gain enough knowledge for the outside-knowledge question-answering task.
Secondly, the methods based on the large-scale LLM (e.g., GPT-3) can achieve good performance. And we can find the methods using better prompts (e.g., Prophet) that can induce the same LLM to achieve better performance. Lastly, our method, transforming the external knowledge into embeddings as a soft prompt, achieves decent performance even with a small-scale LLM.
We further conduct ablation studies on OK-VQA. As listed in Table 1, with regard to the traditional knowledge incorporation approach, we observe a significant drop in performance, which indicates that transforming the external knowledge into a soft prompt improves the instruction of LLM. As for the variant without dynamic fusion, denoted as SKP (w/o dynamic fusion), we see that the performance degrades from 63.3 to 62.4. We conjecture that dynamic fusion adaptively attends to external knowledge, mitigating the influence of cases where all knowledge snippets are irrelevant. Moreover, we perform an experiment without the knowledge supervision signal, denoted as SKP (w/o knowledge supervision signal), and a performance decline is observed, which manifests the effectiveness of the proposed training method in terms of knowledge utilization. And this supervision signal can play a more important role as more knowledge snippets are selected for answer generation because it inevitably introduces more noise and increases the need for extracting pivotal information. In addition, when the variant work without an ensemble-based prediction mechanism, denoted as SKP (w/o ensemble-based prediction), we notice a drop in performance, which validates the necessity of multiple external knowledge. We also verified the significance of design choices with
scipy.stats.ttest_ind(p < 0.05). On the other hand, we conduct experiments on the A-OKVQA dataset, and k is set to 3. The results are obtained based on the direct answer evaluation setting, which is more challenging and realistic. In addition, because our work aims to better utilize relevant knowledge and A-OKVQA provides the relevant knowledge on the training and validation sets, we provide the comparison results on the validation set. As summarized in Table 2, it can be seen that our method performs better than the others on the A-OKVQA dataset. Notably, the performance drops dramatically when engaged with the traditional knowledge incorporation approach. We attribute this to the fact that our design choice endows our model with the robust capability to handle external knowledge.

heading: 4.6 Effects of k
text: This section investigates how our method behaves using a different number of knowledge snippets k on the OK-VQA dataset. Firstly, we set k = {1, 2, 3, 4, 5, 6, 7} to evaluate model performance respectively. As Figure 3 shows, the prediction performance fluctuates as k varies and saturates with k = 3. We conjecture that the prediction under the consideration of multiple external knowledge has a positive effect, but when too much relevant knowledge is involved (k gradually approaches k ′ ), the
knowledge choice space for the proposed method greatly shrinks, and more distracting information messes pivotal knowledge up, leading to the limited performance. Therefore, we empirically set k = 3 and k ′ = 10 in our experiments.

heading: 4.7 Case Studies
text: Figure 4 exhibits two samples of prediction. Specifically, in the first question, “what type of apples is the woman using in this recipe?”, it is obvious that the question needs the help of external knowledge to make an accurate answer (instead of a superficial answer), in which using only the implicit knowledge of LLM does not perform well. Besides, conditioned on the traditional knowledge incorporation method, it remains challenging to generate a correct answer. We conjecture that the traditional approach fails to capture the information derived from external knowledge accurately. In the second question, “which type of metal is used for making this toilet? ”, the answer obtained conditioned on LLM only deviates from the essence of the actual situation. That is, the toilet itself cannot be made of aluminum. So, the wrong answer is caused by the incomplete knowledge contained in LLM, or the accurate knowledge of LLM is not induced. Further, the answer obtained by the traditional knowledgeenhanced method is still not accurate enough, indicating that this paradigm lacks the ability to handle external knowledge accurately.

heading: 4.8 Discussions
text: The prompt method is crucial for eliciting the knowledge of large language models in the output. In our experiments, the proposed prompt method outperforms the ‘SKP(vicunna) w/ traditional knowledge incorporation approach’, which concatenates external knowledge text and image embedding based on projector. This demonstrates that our prompt method can better stimulate the performance of LLM on downstream tasks. Additionally, we conducted an experiment on OKVQA using a typical prompt method that converts the image into textual descriptions instead of using Q-former to transform them into embeddings. The results were very poor (below 50) because this prompt method cannot ensure that all image information is captured and may miss visual details relevant to the question.
Compared to the outside-knowledge based datasets OK-VQA and A-OKVQA, FVQA requires basic factual knowledge to answer questions. We
conducted an experiment on this dataset and found that our method, given the same external knowledge, enables the model to achieve higher performance than traditional methods. However, because the questions in FVQA often require relatively simple external knowledge, the implicit knowledge in LLMs is already sufficient to handle these questions. As a result, utilizing external knowledge does not significantly improve performance.

heading: 5 Conclusion
text: This work proposes a method to extract the pivotal information from the relevant external knowledge, which is then fused with the visual information to form a knowledge-enhanced context. This approach protects LLM from being misled by distracting information, and avoids adding extra input tokens to LLM. The experimental results on knowledge-based VQA benchmarks demonstrate that the proposed method enjoys better utilization of external knowledge and is conducive to more accurate answers. In addition, compared with constructing a large and complex knowledge mining model, which requires lots of labeled training data and faces the problem of generalization, our method enjoys the characteristics of good interpretability, and simplicity of use.
Different from most recent works, we do not focus on constructing a better retriever to obtain more beneficial knowledge. Given a relevant external knowledge, our work focuses on how to better utilize it to improve the prediction performance.
We try our best to excavate its value, but the beneficial information within an external knowledge is limited. In future work, we will study how to combine our method and the retriever construction method to obtain a better performance improvement. In addition, we will also investigate the potential to improve the complementary between external knowledge and model implicit knowledge.
Limitations
Our method focuses on improving the prediction performance of LLM by mining external knowledge, but there is vast implicit knowledge embedded in LLM. Therefore, the problem of how external knowledge and implicit knowledge supplement each other mutually remains tricky. In the case where external knowledge and implicit knowledge conflict, it is necessary for a model to infer the quality of knowledge. The above problems are beyond the scope of this paper and left for research efforts in the future.

heading: Acknowledgements
text: This paper is supported by National Key R&D Program of China (No. 2022ZD0118802), National Natural Science Foundation of China (No. 62206279, 62306314), Jiangsu Key R&D Program for Industry Prospect and Core Technological Innovations Project (No. BE2023016).

heading: Future Work
text: In future work, we will study how to combine our method and the retriever construction method to obtain a better performance improvement. In addition, we will also investigate the potential to improve the complementary between external knowledge and model implicit knowledge. Limitations
Our method focuses on improving the prediction performance of LLM by mining external knowledge, but there is vast implicit knowledge embedded in LLM. Therefore, the problem of how external knowledge and implicit knowledge supplement each other mutually remains tricky. In the case where external knowledge and implicit knowledge conflict, it is necessary for a model to infer the quality of knowledge. The above problems are beyond the scope of this paper and left for research efforts in the future