Investigating and Mitigating Catastrophic Forgetting in Medical Knowledge Injection through Internal Knowledge Augmentation Learning
Authors: Yuxuan Zhou
Xien Liu
Xiao Zhang
Chen Ning
Shijin Wang
Guoping Hu
Ji Wu
Venue: NeurIPS 2025 poster ()
Keywords: Large Language Model
Medical Knowledge Injection
Catastrophic Forgetting
Primary area: deep_learning
TL;DR: We investigate catastrophic forgetting in LLM medical knowledge injection and propose an internal knowledge augmentation learning method to mitigate it.

Abstract: Large Language Models (LLMs) are expected to possess comprehensive medical knowledge to support real-world clinical applications. While domain-specific fine-tuning effectively injects medical knowledge into LLMs, it often causes catastrophic forgetting of previously acquired knowledge and instruction-following capabilities. In this paper, we investigate this issue and reveal a pattern of proximity-dependent forgetting: knowledge that is semantically or topically close to the injected content is more likely to be forgotten, while unrelated knowledge shows minimal degradation. Moreover, we observe that existing mitigation techniques fail to address this type of forgetting effectively. Motivated by this observation and inspired by human learning mechanisms, we proposeInternAL (\Internal Knowledge Augmentation Learning), a novel approach that leverages LLMs' own internal knowledge to mitigate forgetting. InternAL first probes internal knowledge closely related to the injection by prompting the model with questions derived from the injected knowledge. This knowledge is then used to augment the original injection dataset, guiding the model to retain related prior knowledge during training. Experimental results on multiple LLMs (LLaMA, Qwen) demonstrate that InternAL significantly mitigates proximity-related forgetting while maintaining strong knowledge injection performance. Our findings provide new insights into the nature of catastrophic forgetting in medical knowledge injection and highlight a promising direction for robust domain adaptation in LLMs. Code and datasets are available at https://github.com/THUMLP/InternAL.

Investigating and Mitigating Catastrophic Forgetting in Medical Knowledge Injection through Internal Knowledge Augmentation Learning
Yuxuan Zhou', Xien Liu"1, Xiao Zhang', Chen Ning', Shijin Wang", Guoping Hu²,JI W3..3.4
*Department of Electronic Engineering, Tsinghua University, Beijing. China FFFLYTEK Research, Hefei, China *College of AI, Tsinghua University, Beijing, China "Beijing National Research Center for Information Science and Technology, Beijing, China
Abstract
Large Language Models (LLMs) are expected to possess comprehensive medical knowledge to support real-world clinical applications. While domain-specific fine-tuning effectively injects medical knowledge into LLMs, it often causes catas- trophic forgetting of previously acquired knowledge and dnstruction-followiit pabilities. In this paper, we investigate this issue and reveal a pattern of proximity- dependent forgetting: knowledge that is semantically injected content more likely to be forgotten, while unrelated knowledge shows minimal degradation. Moreover, fail to address this type of forgetting effectively. Motivated by this observation and inspired by human learning mechanisms, we propose InternAL (Internal Knowledge Augmentation Learning), novel approach that leverages LLMs' own internal knowledge to mitigate forgetting. InternAL first probes internal knowledge closely related to the injection by prompting the model with questions derived from the injected knowledge. This knowledge then used augment the original injection dataset, guiding the model to retain related prior knowledge during train- ing. Experimental results on multiple LLMs (LLaMA, Qwen) demonstrate that InternAI. significantly mitigates proximity-related forgetting while maintaining strong knowledge injection performance. Our findings provide new insights into the nature of catastrophic forgetting in medical knowledge injection and highlight a promising direction for robust domain adaptation in LLMs. Code and datasets are available at https:://// //ggttttubb wttpttuu...ootuu.PPP... Cowm/HHUMLP/Iterrr..
topically close to the
observe that existing mitigation techniques
Introduction
Large language models (LLMs) achieve remarkable success across a wide range of domains 1 and exhibit great potential in specialized fields such as medicine. However, unlike general tasks, solving real-world clinical problems demands deep understanding of domain-specific knowledge. While general-domain LLMs encode substantial world knowledge through pretraining and perform well on certain medical benchmarks 16 77. recent studies [8] 9 suggest that their medical knowledge remains inadequate for supporting real-world clinical applications. Such findings highlight the need for effective strategies to inject essential medical knowledge into LLMs.
Existing posttaretnannin knowledge injection methods can be broadly categorized into Inference-time injection and Fine-tuning -based injection. Inference-time injection methods [10H13], often realized through
Generation (RAG), retrieve relevant knowledge from external sources and integrate it into the model's inference process. These methods effectively provide up-to-date
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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(+) Catastrophic Forgetting is Proximity-Dependdnt
(b) Our Method: Internal Knowledge Augmentation Learning
Knowladge Injection
Injected Knowledge
Injection
/Covid-12 19, has symptom couple
(Corid 19, has sympton,, cougly
Augment
Model Mo
ModelM,
Generate
Model Ma
Model M
Probing Question
Perfomance
(What are the common syaptimnc of Covid-197)
Performance
Self Generate
Similarity to injoction
Similority,
Covid-19, Covid-19, has groptoer has yosptoer has syseptous. congt
fener
Hepertoncion, Waskington D.C..
Headoche
capasi of United Stotes
Internal Related Knowledge
(Cissid-19, 19, foo sympion, fivver)
Milgate Forgetting wilth LLM internal Ansowlodge!!
Figure I: Left: Catastrophic forgetting exhibits proximity-depender pattern: knowledge closely related to the injected knowledge is more likely to be forgotten: Right: Our proposed Internal Knowledge Augmentation Learning (InternAL) method for mitigating catastrophic forgetting.
knowledge to LLMs, but their performance heavily relies the quality of the retrieved content and may fail when the knowledge is required implicitly. On the other hand, fine-tuning-base injection methods [44 train the model datasets containing the target knowledge through fine-tuning, enabling the model to effectively apply the injected knowledge both explicitly and implicitly. However, such methods often suffer from forgetinniic [17), where the model forgets previously acquired knowledge and
following abilities after fine-tuning, Though several methods (1424] have been proposed to mitigate catastrophic forgetting, their effectiveness in the medical domain has not been thoroughly investigated.
In this paper, focus on fine-tuning- -ased methods and investigate the problem of catastrophic forgetting in medical knowledge injection. Specifically, we aim to address the following research questions: RQI: What kind of knowledge more likely be forgotten during knowledge injection? RQ2: How effective are existing methods in miligating catastrophic forgetling? and RQ3: How to effectively miligate catastrophic forgetling in medical knowledge injection?
To answer these three research questions, we first conduct medical knowledge injection based on approximately 20k triples covering 21 types of medical knowledge extracted from the large-scale medical knowledge graph PrimeKG [25], and evaluate the injected model on a series of general and medical benchmarks. We observe that catastrophic forgetting exhibits a proximiry (illustrated in Figure [a): knowledge closely related to the injected knowledge is more prone to forgetting, while knowledge that is more distant tends to be less affected. Moreover, existing mitigation methods show limited effectiveness, especially in preserving knowledge that is highly related to the injected content.
pattern
Motivated by these findings, we further propose a novel method (depicted in Figure [b) called Internal Knowledge Augmentation Learning (InternAL), first attempt mitigate catastrophic forgetting by leveraging the related internal knowledge of the target LLM. Specifically, we first extract relevant knowledge from the target LLM by prompting the LLM with questions generated based on the injected knowledge. We then incorporate this retrieved internal knowledge into the original injection dataset and fine-tune the model on the augmented data, thereby improving its ability to retain prior knowledge that is closely related to the injected content. The experimental results on several representative LLMs (e.g., LLaMA, Qwen) demonstrate that method significantly mitigates forgetting of prior knowledge, particularly for knowledge that is closely associated with the injected content. Our contributions be summarized as follows:
We investigate the problem of catastrophic forgetting in medical knowledge injection and reveal a muximity-dependenn forgetting pattern, where knowledge closely related the injected knowledge is more likely to be forgotten.
We evaluate several existing methods for mitigating catastrophic forgetting and find that they are not effective enough in the medical domain, especially in retaining relevant medical knowledge.
We propose InternAL. novel method that augments the injection process with internally re- trieved knowledge from the L.M itself. Our method significantly alleviates forgetting, especially for knowledge that is semantically proximate the injected content.

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2 Related Work
Knowledge Injection Existing knowledge injection methods can be categorized into the following two types: (1) Inference-time injection [10433 (i.e. RAG) methods incorporate knowledge retrieved from external sources at inference time, enabling LLMs to access up-to-date information without additional fine-tuning. However, applying RAG in the medical domain presents several challenges, such as the difficulty of aligning queries with domain-specific content and the inability to retrieve represent implicit knowledge that is required in many clinical reasoning tasks; (2) Fine- Fine-tuning- based injection 7 [4)16] methods train LLMs on datasets containing the target knowledge through fine-tuning. However, these methods often lead to catastrophic forgetting on prior knowledge and instruction-followimn abilities. In this paper, we aim to investigate and mitigate the catastrophic forgetting problem in the medical domain.
Mitigating Catastrophic Forgetting Existing studies on mitigating catastrophic forgetting can be categorized into three types: (1) Replay- methods [8 19] which alleviate catastrophic forgetting by replaying old knowledge during training. This is typically achieved by mixing knowledge-injectinn samples with original training data; (2) Farameter-Efficice
Fine-Tuning (PEFT) methods (20] 21], which mitigate forgetting by freezing most of the model parameters and updating only a small subset during fine-tuning: (3) Knowledge editing methods (2 24, which aim to inject new knowledge by first locating the relevant representation regions in the model and then performing small-scale parameter updates in those regions. In this work, we further investigate the effectiveness of these methods in mitigating catastrophic forgetting within the medical domain.
Internal Knowledge Awakening There are also some studies that activate the internal knowledge of LLMs to improve their performance knowledge-intensing tasks. These methods typically leverage prompting techniques [26] or fine-tuned small language models (27) to guide the LLMs to recall and utilize their internal knowledge in the reasoning process. The main difference between these methods and our work is that these methods focus on improving the model's performance on knowledge-intensivy tasks, while our work aims mitigate catastrophic forgetting by augmenting the knowledge injection process with relevant internal knowledge.
3 Catastrophic Forgetting is Proximity-Dependent
To mitigate catastrophic forgetting during medical knowledge injection, it is essential to first in- vestigate which types of knowledge more susceptible to forgetting (RQ1) and whether existing mitigation strategies are effective enough in this domain (RQ2). We begin by formulating the problem, and then describe our experimental setup, results and detailed analysis to answer these questions.
3.1 Problem Formulation
Suppose we are given an LLM Mo and a set of knowledge triplets Kinject = [1h,, ri, 4)) to be injected, where has ri, and t; denote the head entity, relation, and tail entity of the jth triple, respectively. The basic optimization objective of a fine-tuning-bast based knowledge injection process can then be formulated as follows:
N Σ
M1 = fuject (Muje = arg max
log (PM (talhs,r:))
(1)
M
where Sinject is the knowledge injection process, PM (t, r;) is the probability of predicting the tail entity given the head entity h; and relation r; using the model M. We denote the model after injection as M1. To measure the forgetting of prior knowledge caused by the knowledge injection process, we can evaluate the model Mi I on an external benchmark Duest which contains M test samples {(T)) 3 Then the catastrophic forgetting of prior knowledge can be measured by:
F F(M MM: Dreat) =Spma (M1) - Sp (Mo)
(2)
F(M][MM) Sp (Mo) Spmm (M1)
RF(Mi Mo: Drest)
(3)
Sp... (Mo)
SD (Mo)
3

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where Spewe (M) is the performance (e.g., accuracy, fl-score, etc.) of the model M on the test dataset Prest F(M,|Mo denotes the absolute forgetting of prior knowledge, and RF (MM,)lM denotes the relative forgetting-i.e. the proportion of performance drop relative to the original model.
One of our core questions (RQ1) is to investigate what types of knowledge are more vulnerable to forgetting during medical knowledge injection. Prior work [22. 23 has shown that knowledge representations in LLMs exhibit locality, where highly-related facts tend share representation space. Inspired by this, we hypothesize that the proximity between the injected knowledge and the knowledge embedded in the test set Duest. denoted by Sim Sim(Cinject. Krest), significantly influences the extent of forgetting F(M21 Mo Drest) on the test set. We will validate this hypothesis through experiments in the following sections.
3.2 Experimental Setup
Datasets For Knowledge Injection We leverage PrimeKG [25], a comprehensive biomedical knowledge graph that integrates knowledge from 20 curated biomedical knowledge bases (e.g.. UMLS 28 DrugBank (29)) PrimeKG encompasses over 4 million triples spanning 29 diverse types of medical knowledge, making it rich and representative resource for medical knowledge injection into LLMs. In our study, we select 21 important categories of medical knowledge, such as disease phenotypes, drug indicationscoonraaniiiattoossssi Considering the large scale of PrimeKG, we randomly sampled ~ Ik triples for each type of knowledge, resulting in a total of 20,864 triples. To identify which knowledge should be injected and to evaluate the effectiveness of the injection, generate Kuest four-option multiple-choice questions (fg) kvaa) for each sampled knowledge triple 2f These questions are designed to evaluate the model's 's basic understanding of the corresponding knowledge. We then evaluate the original model Mo using these questions and measure its accuracy (Moo) on each knowledge triple 2,:
effects, and protein functions/interactions
Rireet
(cc;(M)- =
(P)
(4)
Kieest
where Pi (MV) is the predicted answer of the model M for the 5hh question of corresponding to 24, 13 is the label of q', and I(.) the indicator function. Knowledge triples with an accuracy below 0.25 (i.e., lower than random guessing on 4-option questions) are selected to construct the injection set Kinject-
Based on this, we construct corresponding test set Pijject evaluate the effectiveness of knowledge injection. We also create two complementary test sets: (1) triples with accuracies above 0.75 are used to build test set Deval for assessing knowledge forgetting; and (2) based on all 20,864 sampled knowledge triples, we further construct comprehensive test set Diotal to evaluate the overall effectiveness of the knowledge injection process. Further details on the construction process and statistics of the injected dataset are provided in appendix A
{qr 2 E KKinjeet, 1 S j S kiest to
Injection Sample
Injected Knowledge
(Loracurbef, indication, bronchitis)
(Loracarbef, indication, bronchitis)
For list of medical entities A: leprosy, B: VIPoma, bronchitis, glycogen storage discuse, E: F: among the given list, the indications of Loracarbef include option
Negative Sampling (Lorscarbef, -indication)
ViPoma, leprosy, glyeogen storage disease,
External KG
Figure 2: An overview of the Reference-style Knowledge Injection (RefInject) method.
Knowledge Injection Method We develop a knowledge injection method named Referencing-stye Knowledge Injection (RefInject) that converts structured knowledge triples into natural language instances suitable for fine-tuning Specifically, for each triple 2, = (h2, r., tt). we sample m - 1 the from PrimeKG, and construct referencing-sty stration (see Figure 2 The LLM learns to predict the correct option (underlined in the figure) corresponding the trruh tail entity f, among candidates. To prevent the model from exploiting superficial patterns (e.g., entity co-occurrence). we follow the method proposed in (30
negative tail entities g gues
demon-
We generate multiple test questions for each knowledge triplet to ensure the robustness of evaluation results.

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and generate & diverse samples for each triple, with the correct answer randomly assigned to different positions across samples. We set 10 and 20 in our experiments. larger values yield diminishing returns. More details (e.g., hyperparameters) can be found in appendix B
Baselines For Mitigating Catastrophic Forgetting We construct representative baseline method to mitigate catastrophic forgetting during knowledge injection, including: (1) General-domait Fine-Tuning (GenFTT): continual fine-tuning on general-domain instruction data to restore instruction- following ability. In our study, we use the MMLU development set (285 examples) for SFT; (2) Parameter- Efficiien Fine-Tuming (PEFT): we apply LoRA which updates only a small subset of parameters; (3) Knowledge Editing: we adopt MEMIT (3 and AlphaEdit [24), both achieving state- of-the-art performance editing factual knowledge. More implementation details of the baseline methods ge.g., hyperparameters setting, training epochs, etc.) are provided in appendix C
Evaluation Benchmarks To investigate what type of knowledge is more likely be forgotten, we evaluate the forgetting of the injected model on a series of general and medical benchmarks. Specifi- cally, we leverage MMLU (3] Non-medical subset, denoted MMLU-O), ARC-challenge [32 (ARC-C) and ConmonSenseQAA 33 (CSQA) to evaluate the model's performance on general knowledge. For the medical domain, we utilize MedQA (34), MMLU medical subset (MMLU- Med). The details of these benchmarks as well as the evaluation settings (prompt formats, inference hyperparameters) are provided in appendices [ndd H
Backbone Models We primarily conduct experiments on four well-known LLMs: Llama3-8B 3 and Qwen3 1.7B, 8B, and 32B [35] chosen for their availability and strong performance range of general-domain tasks. Due to resource constraints, we conduct full-parameter fine-tuning only on the smaller models (Llama3-S8) 8B and Qwen3-1.7. 8B), and apply LoRA the largest model (Qwen3-32B) In our study, we use the instruction-tura version of these models.
3.3 Results and Analysis
LLaMA3-8B
Qwen3-1.78
Qwen3-8B
Qwen3-32B
Relative Performance
60
2
2
MedOA
MMLU-MMe
CSQA
Figure 3: Relative performance (%) of LLMs on evaluation benchmarks after knowledge injection, normalized to their original performance. Error bars represent the standard deviation across 3 runs.
Knowledge Closer to Injected Facts Is More Prone Forgetting We perform knowledge injection on the selected LLMs using the RefInject method and evaluate forgetting across evaluation benchmarks. The relative performance (normalized to the performance before injection) of the injected models is shown in Figure 3
We observe that the performance of the injected models on all the medical benchmarks (Deval, MedQA and MMI.U-Me) drops significantly, while the performance on general benchmarks (MMLU-O, ARC-C, CSQA) remains relatively stable. For example, Llama3- 8B experiences a >30% drop in relative performance on the MedQA benchmark. while it retains over 90% of its original performance all the general benchmarks. Such phenomenon indicates that LLMs are prone to forgetting knowledge in domains closely related to the injected content-suue as the medical domain in this study-dune the injection process.
To investigate the phenomenon of divide the medical benchmarks into proximal and relatively distal sets based on the knowledge
forgetting a more fine-grained view, we
'Replay- based methods are not applicable in our study, because the old data for pretraining and instruction fine-tunin mine-tuning existing LLMs is typically not publicly available.
5

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proximity between the injected knowledge set Diject and the evaluation samples. For the PrimeKG: based evaluation set (Deval), samples are classified proximal if they share the same head entity and relation with any injected knowledge triple; otherwise, they are considered distal. For MedQA and MMLU-Med, where explicit knowledge triples per test sample are unavailable, we embed both the test samples and the injected knowledge into a shared space using the MedEmbed model [36], and compute cosine similarity to estimate proximity. A threshold of 0.8 is then used to select samples that are considered proximal. The detailed splitting process is provided in appendix P
Table 1:
Performance (%) of the original and injected models on medical benchmarks, divided according to the proximity the injected knowledge.
Deval
MedQA
MMLU-Med
Model
Proximal
Distal
Proximal
Distal
Proximal
Distal
Llama3-8B
88.9
91.9
56.0
48.8
84.0
68.1
+Knowledge Injection 51.2 4%
61.9 43..6%%
35.1
34.0
64.1 24.1_23.70 _23.7% 7%
53.4
Qwen3-1.7B
86.8
89.2
40.3
36.4
68.9
57.7
+Knowledge Injection 56.2,35.33
66.0 66.0,26.0%
29.0
27.9
47.9423.4%
49.9.27.60 .27.6%
48.5
Qwen3-8B
88.7
91.8
64.7
56.1
92.2
77.4
+Knowledge Injection 64.0g27.89
74. 74. 119.3%
51.6120.3%
47.
116.2%%
84. 8%
71.4
Qwen3-32B
89.0
92.8
72.2
67.7
93.0
80.4
+Knowledge Injection 63.3 128.8% 65.5 (29.5%
59.6117.5%
59.3
80.0.14.0%
74.7 14.7%
Table presents the performance of the original and injected models on the medical benchmarks, divided into proximal and distal subsets, with subscripts showing the relative forgetting. Across all datasets, we observe that the forgetting of proximal knowledge is generally more severe than that of distal knowledge. For example, the case of Llama3-8B, the relative forgetting on proximal knowledge is 42.4, 37.4, and 23.7 across benchmarks, while it is only 32.6, 30.4 and 21.6 for distal knowledge. These results validate our hypothesis that the proximity between the injected knowl- edge and the test samples significantly influences the extent of forgetting, and that knowledge that is more closely related to the injected knowledge is more likely be forgotten.
Table 2: Performance (%) of the original (Llama3-8BB and injected models using various methods on the medical and general benchmarks.
Medical
General
Model
Diotal
Dinjoct
D Deval
MedQA
MMLU-Med
MMLU-O
ARC-C CSQA
Original
51.5
9.7
91.4
50.7
69.8
59.8
75.4
66.4
MEMIT
53.4
36.9
75.9
48.0
66.2
58.3
75.0
65.3
AlphaEdit
52.3
32.7
77.1
44.7
64.9
57.4
73.9
64.8
RefInject
65.0
77.4
60.3
34.2
54.5
53.8
69.7
64.9
+LoRA
66.9
75.9
65.3
36.7
55.3
55.6
72.1
65.0
+GenFT
68.8
73.4
71.4
41.8
64.0
59.6
76.0
69.3
Existing Mitigation Methods Are Not Effective Enough for Knowledge Closely Related to Injected Knowledge We further investigate the effectiveness of methods for mitigating catastrophic forgetting in the knowledge injection process, with the results on Llama3-8B summarized in Table (full results are provided in appendix G). While knowledge editing methods (MEMIT, AlphaEdit) retain original knowledge well, their performance on injected knowledge is poor (36.9 and 32.7 on Dinject,, resulting in limited overall injection effectiveness (+1.9 and +0.8 on Dtotal). A possible rea- son that these methods modify only a limited number of model parameters, which may insufficient for enabling LLMs to generalize the injected knowledge effectively. In contrast, LoRA and GenFT retain most RefInject's injection effectiveness, achieving accuracies of 75.9 and 73.4 on Dinject and overall performance of 66.9 and 68.8 on Dtntal, respectively. While these approaches also mitigate forgetting of the original knowledge to a certain degree, notable degradation remains, particularly on medical benchmarks. Notably, fine-tuning with general-domain instruction data (GenFT) effectively

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restores most of performance on general-domain datasets, but forgetting on medical benchmarks persists ge. 41.8 vs. 50.7 on MedQA). Our findings suggest that though the catastrophic forgetting of knowledge injection can be mitigated to some extent by existing methods, they are not effective enough regarding either the injection effectiveness or the retention of original knowledge.
4 Mitigating Catastrophic Forgetting via LLMs' Internal Knowledge
4.1 Methodology
Schema of Internal Knowledge Augmentation In this section, we propose a novel method called Internal Knowledge Augmentation Learning (InternAL) as first attempt to mitigate catastrophic forgetting by leveraging related internal knowledge from the target LLM. An overview of the proposed method is presented in Figure 4 Our findings in the previous section indicate that knowledge more closely related to the injected content is particularly susceptible to forgetting. To address this, we first extract the relevant knowledge from the target model Mg:
Kinner = Sprabe Xinjoct)
(5)
where Sprobe is the probing function that extracts the internal knowledge relevant to Kinject from the model Mo. Then, the original knowledge injection process can be augmented with the internal knowledge Kinner:
M
fang (Mo; Kinject, Kinner)
(6)
By attending to the relevant internal knowledge during the injection process, the proposed InternAL method aims to mitigate the forgetting of the most relevant knowledge to the injected knowledge.
Injection Sample
Original Sample Generation
For list medical eniitics leprusy, VIPomu, C: brunchitis, D: pharvagitis, E: sinusitis, ... amung given the indications Loracarbef includo option C, D.
Injected Knowledge (Loracarbof. Indication, bronchitiss)
Internal-aware phorynggnns. Augmentation
sinusitis,
Internal Knowledge Probing Module (fprate)
Generate
Pharyngiis and sonsillits, simusitiy
Laracarbef, indication, phuryngitis) (Lornurreef, indication, tomsillitis) (Loracarbef, indication, sinusitis (Loracarbef, Indicationn cellulitis)
Probing Question
mergo
What diseases indications for Loncarbef?
extluliits impotign.
comsillus, Onttss media
Model M.
LLM Self Response
Internal Related Knowledge
Figure 4: Overview of the proposed Internal Knowledge Augmentation Learning (InternAL) method.
Internal Knowledge Probing (probe) To extract the internal knowledge from the target LLM, we develop internal knowledge probing module that first generates probing question Q, based on the head entity h; and relation r; of the injected knowledge triple (hg,r,,44 with templates. Such question is designed to prompt the model to recall all the possible tail entities that have the relation r; with the head entity h4. Then we prompt the target LLM Mo using the probing question K = times generate K different LLM responses R. R2. R Finally, we extract the tail entities from the generated responses and filter out the duplicates to form the internal knowledge set Kinner
Kinner ((b,, t')le' e Sextraa SSig
k=1
where Sextract is the function that extracts the tail entities from the generated responses, implemented by prompting the target I.LM. More details on the probing process, including the templates and generation hyperparameters, can be found in appendix H
7

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Internal-aware Sample Augmentation ( inject) After extracted internal knowledge relevant to the injected knowledge, we augment the sample generation process with the extracted internal knowledge. Specifically, for each injected knowledge triple 2; = (h4, re, t4), we sample n relevant tail entities t1, "2, ... ty from Kinner that share the same head entity and relation with 2,, and sample m n 1 the RefInject method, we
negative tail entities tleg L2 construct a referencing-Seee -tyle demonstration using the sampled tail entities. The LLM is trained to select multiple correct options from m candidates, including both the injected tail entity t, and the relevant tail entities ti. 122 , We keep the m and l consistent with RefInject, and random choose n from O to 3 for each sample to prevent the model from learning statistical biases. The generated samples are then used to fine-tune the target LLM Mo to obtain the injected model Mf Further details on the sample augmentation process can also be found in appendíx H
geeg 'n-n-1 from PrimeKG. Similar
Table 3: Performance (%) of the baseline knowledge injection method (RefInject) and the proposed method (IntemAL). The lowest relative forgetting on each benchmark is underlined.
Medical
General
Model
Dintal D Dinjecs
Docal
MedQA
MMLU-Med MMLU-O
ARC-C
CSQA
Llama3-8B
51.5
9.7
91.4
50.7
69.8
59.8
75,4
66.4
+RefInject
65.0
77.4
60.3
34.2132.6%
54.5
53.8y10.0%
69.747.6%
64.9.2.3%
+RefInject+GenFT
68.8
73.4
71.4
41.8417.6%
64.0g8.3%
59.6,0.2%
76.0go.7%
69.374.45
+IteemAL (ours)
69.3
74.3
70.9
12.5.22.2%
56.8,18.7%
70.3 p8.7...5%
60.9
+ntemmAL+GenFT
71.2
71.4
77.4
45. 111.1%
66. 15.3%
60.4
70.7
69.8
Qwen3-1.7B
42.6
9.7
88.7
37.5
59.0
52.5
71.6
66.4
+RefInject
60.4
63.0
64.1 127.12777%
28. 28.8
49.4
49.3
65.2%1
53. 1200.0%
+eelIFject+GenFFT
62.9
60.4
72.8
31.311
55.0ja.8%
52.1 10.9%
68.
14.9%
63.3
+ntemAAL (ours)
63.5
59.3
75.
115.51
14.7%
51.4 51.4 11.88%
47.6 49.69..4%
19.4%
61.6 13.9%
f123
+ntemAA++GenFT
65.1
58.7
79.2
33.3
58.0g1.7%
12.0
68.514.3%
61.826.9%
Qwen3-8B
49.3
9.4
91.4
58.5
79.0
67.2
87.3
80.3
+RefInject
68.6
72.1
72.2
111100
48.3117.4%
72.7
63.01e.2%
83,414.8%
76.8
Genn
70.0
70.4
16.6.1
50.8
10.8.13.2%
75.644.3%
69. 42.8%
87.3
10.0%
78.6
+InterAAL (ours)
73.2
72.0
82.0
10..
51.1
74.9 15.1%
65.9 11.9%
84.9 12.
76.774.5%
+eneemAL+GenFT
73.7
70.8
84.0g8.0%
52.3110.8%
76.9 16.912.6%
70.2
87.7 10.8%
78.2
Qwen3-32B
59.3
10.2
92.4
68.2
80.8
68.5
86.9
83.5
+RefInject
63.4
69.4
65.2
4%
59.6112.8%
43.3
67.6
85.3
84.6p1.3%
+ +eeIn
66.8
73.6
68.
68.6%25.7%
60.2
19.9
88.8
84.1
InternAL (ours)
66.5
64.7
12.8,21.1%
63.3
11.3%
67.7 11.3%
86.9 16.9%
82.8 10.9%
+ntemAAL+enFTT
73.5
67.7
83.1 110.0%%0%
63.4g7.0%
81.9
72. 72.615.9%
89.993.4%
83.0go.7%
4.2 Results and Analysis
Effectiveness of InternAL across Benchmarks We conduct experiments to evaluate the effec- tiveness of the proposed InternAL method in mitigating catastrophic forgetting during knowledge injection, comparing it against the original RefInject method, both with and without general-domale Fine-Tuning (GenFT). The results are presented in Table We observe that InternAL significantly alleviates catastrophic forgetting across all medical benchmarks and backbone models, while main- taining stable performance on general-domain benchmarks. For instance, on the MedQA benchmark, Llama3-8B fine-tuned with InternAL. reduces relative forgetting by 10.4 compared to the original RefInject method (22.2 vs. 32.6). Furthermore, InternAL can be combined with GenFT to further mitigate the forgetting of original knowledge For example, Llama3-8B achieves 77.4 on Deval after applying IntemAL+GenFT., reducing relative forgetting by 6.5 compared to RefInject+GenFT (15.4 vs. 21.9). These results suggest that while general-domaii instruction tuning (GenFT) effectively re- stores the model's instruction-followe followiig capability, applying the proposed InternAL. method provides additional gains in preserving the original knowledge.
Effectiveness on Proximal vs. Distal Knowledge We investigate the effectiveness of the proposed Al. method on the proximal and distal subsets of the medical benchmarks, with results presented in Table 4 We observe that though InternAL can effectively mitigate forgetting both proximal
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Table 4: Performance (%) of RefInject and InternAL on proximal and distal subsets of medical benchmarks (using Llama3-8B as backbone).
Deval
MedQA
MMLU-Med
Model
Proximal
Distal
Proximal
Distal
Proximal
Distal
Llama3-8B
88.9
91.9
56.0
48.8
84.0
68.1
+RefInject
51.2.j42.4%
63%.%%
137.4%
33.9 130.6%
64. 64.1123.79
53.4
+RefInject+GenFF
72.9120.00 J2.4_29.8%
6%
45.8
40..11
76.7
18.7%
62.5 18.3%
+InternAL (ours)
63.6 _2..5%
72.2 12..4
43.1
123.2%
12..9
66.7
120.7%
55.6 1%
+InternAL+GenFFT 71.2 71.2
48.4414.7%
50.0410.7%
43.3
411.3%
80.3 80.341.5%
64.415.1%
Relative Performance (96)
Deval
Relative Performance (96
MedQA
Rel Relative Performance (96
MMLU-Med
100
1000
1000
80
80
80
2000
4000
6000
8000
0
2000
4000
6000
8000
D
2000
4000
6000
8000
# Injection Samples
# Injection Samples
# Injection Samples
Relative Performance (%)
MMLU-O
Relative Performance (%)
ARC-C
Relative Performance (%)
CSQA
100
100
100
80
80
80
2000 4000
6000
8000
0
2000
4000
6000
8000
0
2000
4000
6000
8000
# Injection Samples
# Injection Samples
# Injection Samples
Refinject
Reflrject+GenFT
InternAL
IntemAL+GenfT
Figure 5: Relative performance (%) of Llama3-8B trained with different knowledge injection methods on various evaluation benchmarks, with varying numbers of injected knowledge triples. All results normalized to the model's performance prior to injection.
and distal subsets, the mitigating effect is more pronounced on the proximal subset. For example, on the MedQA benchmark, InternAL reduces relative forgetting by 14.2 on the proximal subset (37.4 vs. 23.2) and 8.7 on the distal subset (30.6 vs. 21.9). This indicates that the proposed method is partieularly effective in preserving the knowledge that is more closely related to the injected knowledge, which is consistent with our hypothesis.
Effectiveness across Injection Scale We further validate the proposed InternAL method across different scales of knowledge injection by conducting experiments with varying ratio of injected knowledge the original injection set Kinject (i.e., 0.2, 0.4, 0.6, 0.8). The results presented in Figure 5 show that the proposed InternAL. method generally outperforms the original RefInject method across all scales of knowledge injection and the performance drops much slower than RefInject on medical benchmarks. This indicates that internal knowledge augmentation better preserves essential medical knowledge the scale of knowledge injection increases.
Representation-LLveè Analysis To further understand how InternAL mitigates catastrophic forget- ting, we analyze the representation changes before and after knowledge injection based on Llama3-8B and Qwen3-8B. Then, we compute the average representation shift on the evaluation set Deval to quantify the extent of representation change caused by knowledge injection on the unlearned knowl- edge. The results are presented in Figure We observe that InternAL. consistently results in smaller average representation shift than RefInject, especially on the middle layers, which are known to capture more knowledge-related information [23] This suggests that InternL effectively preserves

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Average Cosnne Distance 1-Smmil (iiilarty))
Llama3-68 8B
Qwen3-88
0.25
Refinject
0.10
Refinject
InternAL (ours)
InternAL (ours)
0.20
0.08
0.15
0.10
0.04
0.05
0.02
0.00
0.00
10
15
20
25
5
10
15
20
25
30
Layer
Layer
Figure 6: Average representation shift on the evaluation set Deval after knowledge injection using RefInject and InternAL methods.
the original knowledge representations during the injection process, thereby mitigating the forgetting.
4.3 Extended Discussion
Impact on Hallucination Level Although the augmented knowledge used in InternAL is generated by the target LLM itself and may contain factual errors, its hallucination level is inherently limited by the model and therefore does not introduce additional hallucinations. To validate this, conduct human evaluation on the hallucination level before and after knowledge injection, and the results in appendix show that the proposed method does not further increase the hallucination level.
Generalizability Other Domain While our study mainly focus on the medical domain, the proposed InternAL method may also be applicable to other domains. To verify this, we conducted small-scale study in the human geography domain. Results in appendix | show that InternAL effectively mitigates catastrophic forgetting during knowledge injection in this domain as well, indicating potential generalizability.
Generalizability to Other Knowledge Formats While our method primarily focuses on structured knowledge, can also be extended other formats, such as unstructured texts. To test this, conducted a preliminary study on free-form medical texts (e.g., clinical guidelines) and adapted InterAAL for this setting. Results in appendix BernndixKKKhooo showow that InternAL effectively mitigates catastrophic forgetting here well, demonstrating its potential generalizability on unstructured data.
Conclusion
In this paper, we explore the challenge catastrophic forgetting in large language models during medical knowledge injection. Our experiments reveal clear proximity-depende forgetting phe- nomenon: knowledge that is semantically or topically close to the injected content more prone be forgotten. We evaluate several existing mitigation techniques and find them inadequate in preserving knowledge that closely related to the injected knowledge. Knowledge Augmentation Learning (InternAL), novel approach that leverages the LLMs' internal knowledge to enhance the injection process. Experimental results show that InternAL. consistently mitigates forgetting across diverse medical benchmarks while preserving most of the injection ef- fectiveness. We hope our findings shed light on the underlying properties of catastrophic forgetting in medical knowledge injection and highlight promising direction for future work that harnesses LLMs' internal knowledge to address this issue.
address this, we propose Internal
Limitations. There are two main limitations in our work. First, our study mainly focus on the catastrophic forgetting in the medical domain, and the behavior of catastrophic forgetting other domains may differ (though we have conducted some preliminary studies in other domains as discussed appendix []. Second, while we propose novel method to mitigate catastrophic forgetting, it is still far from completely resolving the catastrophic forgetting problem. Future work should focus on extracting more relevant internal knowledge and developing more effective augmentation methods to mitigate catastrophic forgetting in knowledge injection.
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Acknowledgments
We thank the anonymous reviewers for their insightful comments and suggestions. This work was supported by Noncommunicable Chronic Diseases-National Science and Technology Major Project (Grant No. 20232D0500501)..
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A Details of Dataset for Knowledge Injection
PrimekG"Iis is large-scale biomedical knowledge graph that contains over 4 million triples, covering wide range of medical concepts and relationships. It is constructed from 20 different biomedical knowledge bases, including UMLS, DrugBank, OMIM, and others. In our study, we utilize PrimeKG to construct dataset for knowledge injection and evaluation of catastrophic forgetting. An overview of the dataset construction process shown in Figure 7
Conolccting injection
Kingert
Acc: 25
Evaluating Injection Effectiveness
3 Piuject
Sample
Test
PrimeKG (-4M triplets)
Ma
Evalusting Catastrophic Forgetting
Sampled Set [20,86 triplets)
cholce questions each)
Knowledge Injectioo Evaluation
Figure An overview of the dataset construction process based on PrimeKG.
To support our study, we selected 21 crucial knowledge types from PrimeKG as listed in Table 6 and randomly sampled 1 1,000 triples from each type given the large scale of PrimeKG. To identify knowledge not well acquired by the L.L prior to injection, we first generated multiple-choice questions (MCQs) for each sampled triplet and evaluated the original model Mo based its performance. An example of the question generation process is shown Figure g with templates provided in Table 5 For each triplet, created ktest 20 questions, each comprising one correct answer (the triplet's tail) and three distractors randomly sampled from PrimeKG.
Triplets on which the model scored below 25% (i.e., below the random- guessing threshold) were selected for knowledge injection, and the corresponding MCQs were used evaluate whether the LLM successfully learned the injected knowledge. To evaluate catastrophic forgetting. additionally constructed a test set comprising triplets where the model scored above 75% on the generated questions, since these triplets are likely to be well learned by the model. Detailed statistics for both injection and evaluation are summarized Table
Farber lipogranutomatosis, Phenotype present, Myoclonus
Question: Which following phenotypes likely characteristic phenotype of Farber lipogranulomatosis?
Options:
Negative Sampling
A: Stressinfeciooninduced laclic acidosis
Undetectable
potentials
C: Shortening of all proximal phalanges of the fingers
Stressinfectton-nnduuedd lactic acidosis,
Myoclonus
PrimeKG
Figure 8: An example of generating a multiple-choice question based on a knowledge triplet.
'PrimeKG is licensed under MIT License.
21

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likely to interact Table 5: Question templates and injection references for constructing the injection and evaluation datasets.
Relation Type
Question Template
Injection Reference
protein-interact with-proten
Which of the following proteins is
proteins that can interact with [head]
likely interact with [head]?
drug-has carrier-protein
Which of the following, proteins
carriers of [head]
most likely the carrier of [head]?
drug-has enzyme-protein
Which of the following proteins
enzymes of [head]
likely enzyme of [head]?
drug-has turget-prolein
Which of the following proteins
turgets of [head]
most likely the target [head]?
drug-has has consspotter-roteed
Which of following proteins is most likely the transporter of [head]?
transporters [head]
drug-has contraindication-de discase
Which the following discases likely prohibits the [head]?
contraindications of [head]
drug-has indication-diseaee
Which of the following diseases indication [head]]
indications of [head]
drug-has off-lahel use-diseuse
Which of the following diseases is most likely an off-lahel use of [head]?
off-label uses of [head]
drug-interact with-drug
Which of most likely has interaction with [hcad]?
following drugs
drugs that have interaction with [head]
protein-associated will--phnotlype
Which of the following pheno- types is most likely associated with [head]?
phenolypes that associate [head]
discase-phenotype present-phenotyy
Which following phenotypes is likely characteristic pheno- type of [head]?
phenotypes [head]
protein-asociated with-disease
Which of the following diseases is most likely associated with [head]? Which of the following effects most likely a characteristic side ef- fect taking [head]?
diseases that associate with [head]
drug-side effect-effect
side effects of [head]
protcin-interant with molecular function
Which of the following molecular functions is most likely to interact with [head]?
molecular functions that interact with [head]
protein-interacts with-celltlar component
Which of the following cellular com- ponents is most likely interact with [head]?
cellular components that interact with [head]
protein-interaets with- biological process
Which the following biological
biological processes that interact with [head]
processes with [head]?
exposure-interact with-protein
Which of the following proteins is
proteins that interact with exposure [head]
most likely interact with [head], environmental exposure?
exposure-linked tn-disease
W hich of the following diseases is most likely linked to exposure to [head]?
diseases that are linked to exposuree to [heud]
exposure-interaets with with-biological process
Which of the following biological processes is most likely to interact with exposure [head]?
biological processes that interact with exposure to [head]
protein-interacts with with-ppthwaa
Which of the following pathways most likely to interact with [heud]? In which of following anatom- ical structures the expression [head] most likely present?
puthways that interact with [head]
protein-expression present in-anatomy
anatomical structures where [head] present
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Table 6: Statistics datasets generated from PrimeKG for knowledge injection and catastrophic forgetting evaluation.
#triplets for injection
#triplets for test
Relation Type
Llama3-8B
Qwen3-8B
Llama3-8B
Qwen3-8B
protein-interact interact with protein
461
461
189
237
drug-ha has carrier- protein
carrier-protein
132
447
232
drug-hu has enzyme-protein drug-has target-protein
305
146
395
492
212
242
579
556
drug-has transporter-protein
159
168
506
425
drug-has contraindication-dissee
402
420
243
234
drug-has has indication-diset diseese
759
drug-has off-label use-disease
378
763
239
32
drug-int inttrittract with-drug
384
431
254
231
protein associated with- trth-phenotype
440
440
263
245
discasc-phenotyy present-phee phennttee
312
362
393
protein cosoiinassociaate with-disease
483
477
264
245
drug-side effect-effect
316
357
311
291
inteinninteeaats with-molecular function
47
69
768
780
protein-interacts with-cellular component
351
487
350
283
protein-interace interacts with-biological process
541
exposure-interacts interacts with-protein exposure-
647
622
147
135
sxposure-linked linked to-disease
505
525
183
187
exposure-interaats with with-biological process
435
442
222
218
protein-interace with-pathway
160
159
558
570
protein- exere-expreessoo present in-anatomy
662
597
99
105
Total
709S
7650
7625
7191
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B Details of Knowledge Injection Method
As introduced in Section 3.2, generate eeferencing-style demonstration examples for knowledge injection. An example of the generation process is shown in Figure
Injection Sample
Injected Knowledge
(Loracarbef, Indication, bronchitis)
(Larscurbef, indication, bronchitis)
For list of medical entities A: leprocy, B: VIPoma, bronchitis, D: glyeogen storage discase, E: F: among the given list, the indications of Loracarbef include option C
Negative Sampling
ViPoma, leprosy, glyeogen storage diseuse,
(Loracarbef, -indication)
External KG
Figure 9: An example of generating referencing-style injection samples.
Specifically, for each triplet (hg, rú, 4:), we first generate an injection reference by filling the head entity h; into the template corresponding to the relation ri, as listed in Table [ For the example in Figure() the injection reference is "the indications of Loracarbef". Then, we use the following template generate the injection example for fine-tuning:
For a list of medical entities A: .... B: ... C: ..... ... ... among the given list, [injection reference] include option [answer].
where the options are the tail entity t; and m 1 distractors randomly sampled from PrimeKG and the answer is the option index of the tail entity 4, For each triplet, we generate 20 injection examples, each with a different set of distractors and a different option index for the tail entity. The injection examples are then used fine-tune the I.L.s.
For fine-tuning, we choose Bimma3-$B-Inntruee and Qwen3-8B as backbone models. We use the causal language modeling (CLM) objective, which is to maximize the likelihood of the model generating the answer given the options and the injection reference. We set the batch size to 8, warmup ratio to 0.05, number of epochs to 1 for both Llama3-8B and Qwen3-8B. For learning rate, we set le-5 and 2e-5 for Llama3- 8B and Qwen3-88 3-B respectively, to balance the injection effectiveness and catastrophic forgetting. We use the Adam optimizer with weight decay of 0.01 and cosine learning rate scheduler. The training is performed on a single NVIDIA A800 GPU with 80GB memory. single fine-tuning process takes about 3 hours for Llama3-8B and hours for Qwen3-8B.
C Details of Baseline Methods for Mitigating Catastrophic Forgetting
We have implemented several baseline methods for mitigating catastrophic forgetting, including knowledge editing methods (MEMIT and AlphaEdit), general-domain finetuning (GenFT), and parameter-efficient finetuning methods (LoRA).
For knowledge editing methods, we follow the original implementation of MEMIT and AlphaFddit to generate a set of editing templates for each knowledge type, as presented in Table 7 To deal with the case of multiple correct answers, concatenate the correct answers into a single string, separated by commas. For hyperparameters, we varied the batch size across [100, 500, whole dataset] and the learning rate across [le-1, 5e-1]. J. We found that batch size of the whole dataset and learning rate of 5e-1 achieved the best performance for both MEMIT and AlphaEdit on our datasets.
For GenFT, we used the development set of the MMLU benchmark that includes a total of 285 examples. We conducted a grid search across different number of epochs [1. 3, 5) and learning rates [2e-5, le-5, 5e-6), and found that 3 epochs with a learning rate of 1e-5 achieved the best performance. The other hyperparameters were set the same values used the knowledge injection process.
For LoRA, we set the rank to 16 and alpha to 32 to balance the performance and the number of trainable parameters. We also set the dropout rate to 0.05 and the batch size 8. The learning rate was set to 3e-05 to reach a similar injection effectiveness as the full- aall-parameter finetuning for fair comparison. The other hyperparameters were set to the same values used in the knowledge injection process.
$ 1-Jama3-8-- 8n-ma---- licensed under Ilama3 License. 'Qwen3-8B BB is licensed under Apache-2.0 2.0 License.
24

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Table 7: Templates for generating samples utilized in knowledge editing baselines.
Relation Type
Editing Template
protein-interact with-protein
[head] can interact with the following proteins:
drug-has carrier-protein
[head] can he carried by the following proteins:
drug-hat has fryyme--rotein
[head] be metabolized by the following enzymes:
drug- gaa target- protein
target-protein
[head] targets the following proteins:
drug-has transporier-protein
[head] is transported by the following proteins:
drug-has Contraindication--ieee
[head] has contraindication for the following diseases:
drug- trug-has inlcaationddieaas
[head] is indicated for the following diseases:
drug-hat has off-label use-disease
[head] is used off-label for the following diseases:
drug-interact with-drug
[head] has interaction with the following drugs:
Proocin-usoociated with-phenollye
[head] is associated with the following phenotypes:
sheass-phenotype present- pheenttp
[head] presents with the following phenotype:
protein-associae with-disease
[head] is associated with the following diseases:
drug-side effect-effect
[head] has the following side effects:
protein-interacts with-molecular function
[head] can interact with the following molecular functions:
protcin-interacts with-cellular component
[head] can interact with the following cellular compo- nents:
protein-interaces biologial process
[head] can interact with the following biological pro-
cosses:
exposure-interacts with-protein
Exposure to [head] can interact with the following pro-
teins:
exposure-linked to-disease exposure-interacts with vith-biological process
Exposure to [head] can be linked to the following diseases:
Exposure to [head] can interact with following biological
processes:
protcin-intcracts with-pathway protein-expression present in-anatomy
[head] can interact with the following pathways:
[head] has expression present in the following anatomical structures:
D Details of Evaluation Benchmarks
We select series of publicly available benchmarks to evaluate the catastrophic forgetting of LLMs after knowledge injection in general and medical domains. Specifically, we choose the following benchmarks:
MMLU: A benchmark for evaluating the performance of LLMS on a wide range of domains, including medicine, law, finance, math, and others. In our study, split the original dataset into 2 subsets: (1) MMLU-Med, which includes 1,565 medical-related questions from 8 different categories (anatomy, virology, clinical knowledge, professional medicine, college medicine, medical genetics, high school biology, and college biology); (2) MMLU-O, which includes 12,477 questions from the rest of the dataset.
MedQA: A benchmark that contains 1,273 multiple-choie questions from the USMLE (United States Medical Licensing Examination).
ARC-Challenge: A benchmark designed to evaluate model's ability to perform complex reasoning over science questions. The dataset consists of 1,172 multiple-choice science questions from grade-school standardized tests, filtered to include only those that cannot be answered correctly by simple information retrieval statistical co-occurrence.
CommonSense A benchmark designed to tests model's ability to understand and reason about commonsense knowledge. We utilize the validation our study, which contains 1,221 multiple-choice questions.
25

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E Details of Evaluation Settings
For evaluation, we utilize zero-shot prompting to evaluate the performance LLMs on the selected benchmarks. Specifically, we use the following prompt template for the benchmark with four options:
Question: [question]
Options:
A: [option!]
B: [option2]
C: [option3]
D: [option4]
Your answer format should be like "Answer: [A-D]".
Such prompt designed to guide the model to generate the answer in the required format. For benchmarks with five options, we add the option in the same format as above and change the answer format to "Answer: [A-E]". our experiments, we observed that the LLMs always generate the answer in the required format before and after knowledge injection. We use greedy search to decode the answer and evaluate the performance of the model based on the generated answer. For each benchmark, we report the accuracy of the model.
F Details of Proximity-based Analysis
To evaluate the impact of proximity on the catastrophic forgetting of LLMs, we conduct a proximity- based analysis by splitting the medical benchmarks into two subsets: (1) Proximal: subset of questions that are closely related to the injected knowledge; (2) Distal: a subset of questions that are less related to the injected knowledge.
For the evaluation set generated from PrimeKG (Deval), we select the samples that share the same head entity and relation with any injected triplet as the proximal subset, and the rest as the distal subset:
Deroxi = {ud Devaal VVVj, A(,,r,)) Kinject
s.t. h = Ar=
(8)
Ddistal Deval Deroxi eval
(9)
Such splitting is based on the assumption that the knowledge injection process maximizes the likelihood of the model generating the tail entity given the head entity and relation. Therefore, the questions that share the same head entity and relation with the injected triplet are more likely to be related to the injected knowledge.
For MedQA and MMLU-Med, since the questions are not explicitly related to specific triplets, calculate the soft similarity between the question and the injected knowledge by embedding the question and entities involved in the injected knowledge into shared embedding space. Specifically, we first use the MedFimbed moodemmmemmmmmelllllll model to generate the embeddings. Then, we calculate the soft similarity between the question and the injected knowledge as follows:
max cos(ge, e) + Σ
Noptions max cos(g: Gh
k=1 eEEnject
eEEnject
sim Einject)
(10)
Noptions +
where
Emb(x)) Emb(y)
cos(æ, y) Emb( [2] Emb(y)]lz
(11)
and
Einject
r,t) Kinject } U (t E Kinject)
(12)
and Noptions is the number of options in the question, of is the question content, and 9, is the k-th option. We then split the questions into proximal and distal subsets based threshold of 0.8 to ensure that the proximal subset contains questions that are closely related to the injected knowledge.
"eddEmeed is licensed under Apache-2.0 2.0 License.
26

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G Full Results of Catastrophic Forgetting Evaluation
We provide the full results of the catastrophic forgetting evaluation the medical and general benchmarks Table X Note that we only implement the knowledge editing methods (MEMIT and AlphaEdit) for Llama3-8B, available for Swen3-1.7B. 8B, and 32B. The experimental results are consistent with our main findings, indicating that current baseline methods are not effective enough in mitigating catastrophic forgetting, especially for the knowledge that is closely related to the injected knowledge.
the original implementation of MEMIT and AlphaEdit is not
We also list the results of RefInject and InternAL the proximal and distal subsets across Llama3-8B and Qwen3-1.72, 8B, and 32B in Table 9 The experimental results demonstrate that the proposed InternAL method is effective in mitigating the catastrophic forgetting of knowledge closer to the injected knowledge. The performance RefInject and InternAL. across different injection scales
Table 8: Performance (%) of the original and injected models using various methods on the medical and general benchmarks.
Medical
General
Model
Method
Dosial
D injuec
Deval
MedQA
MMLU-Med
MMLU-O
ARC-C
CSQA
Original
51.5
9.7
91.4
50.7
69.8
59.8
75.4
66.4
MEMIT
53.4
36.9
75.9
48.0
66.1
58.3
75.0
65.4
Llama3-8B
AlphaEdit
52.3
32.7
77.1
44.7
64.9
57.4
73.9
RefInject
65.0
77.4
60.3
34.2
54.5
53.8
69.7
+LoRA
66.9
75.9
65.3
36.7
55.3
55.6
72.1
64.9
+GenFT
68.8
73.4
71.4
41.8
64.0
$9.6
76.0
69.3
Original
42.6
9.7
88.7
37.5
59.0
52.5
71.6
66.4
MEMIT
AlphaEdit
Qwen3-1.7B
RefInject
60.4
63.0
64.1
28.8
49.4
47.3
60.2
+LoRA
59.7
70,2
53.7
25.4
44.7
48.8
62.9
59.4
+GenFT
62.9
60.4
72.8
31.3
55.0
52.1
68.1
63.3
Original
49.3
9.4
91.4
58.5
79.0
67.2
87.3
80.3
MEMIT
AlphaEdit
Qwen3-8B
RefInject
68.6
72.1
72.2
48.3
72.7
63.0
83.4
76.8
+LoRA
67.2
71.9
68.9
45.8
71.2
64.1
84.4
79.0
+GenFT
70.0
70.4
76.6
50.8
75.6
69.1
87.3
78.6
Original
59.3
10.2
92.4
68.2
80.8
68.5
86.9
83.5
MEMIT
AlphaEdit
Qwen3-32B
RefInject (LoRA)
63.4
69.4
65.2
59.6
78.3
67.6
85.3
84.6
+LoRA
+GenPT
66.8
73.6
68.6
60.2
79.9
70.9
88.8
84.1
on Qwen3-8B in also presented Figure The experimental results demonstrate similar trend as that of Llama3-8B, indicating that InternAL is effective in mitigating the catastrophic forgetting of L.LMs across different injection scales, especially for the knowledge that closely related the injected knowledge.
H Details of Internal Knowledge Augmentation Learning
Internal Knowledge Probing To probe the internal knowledge from the target LLM, first gener- ate probing question for each head-relation pair in the injection set ((hhi rs) rr))((, 4) E Kinject } using the probing templates listed in Table I0 We then use the generated probing question to query the LLM K = 5 times, resulting in probing answers for each probing question (R), R?.... Rt)). We set the decoding temperature to 0.6 balance the diversity and accuracy of the probing answers.
Subsequently, we extract the tail entities from the probing answers by prompting the target LLM with the following instruction: "[Extraction Question]. Return list of entities that satisfy the query, separated by a vertical bar (+1'). no entity meet the query, output 'None'. Paragraph: [paragraph]." The extraction question generated based the extraction templates listed in Table 10
Finally, we parse the extracted entities and filter out the entities that are not in the injection set. We then use the extracted knowledge (Kinner) to augment the knowledge injection process.
27

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Table 9: Performance (%) of RefInject and InternAL on proximal and distal subsets of medical benchmarks.
Deval
MedQA
MMLU-Med
Model
Method
Proximal
Distal
Proximal
Distal
Proximal
Distal
Original
88.9
91.9
56.0
48.8
84.0
68.1
+RefInject
51.2,42.4%
61.9 .2..6
15.14477.%
33.9120.6%
64. 14..72.7%
53.4121.6%
Llama3-8B
62.4 8%
72.9
20.0%
45.8
118.2%
40.3
18.718.7%
18.9%
InternAL
63.6,28.5% 5%
72.2 4%
43.1 43.1 120.2%
38.2 9%
66.7 120.7%
55.6118.4%
+eIernAL+GenFT
11.2.19.9%
78.4_ 14.795
50.0
43.3 .3%
80.3
64.415
Original
89.2
37.2
39.5
54.0
+RefInject
56.2.g5..5
66.0_26.0%
(22.495
28.3
49.8 19.8
48. 48.1111.0%
Qwen3-1.7B
+RefInjoc=+GenFT
65.4124.7%
74.6, 74.6,16.44 10.44%
.16.0%
31.0121.05 .%
55.618.4%
53.0 89%
+IntemAL
69.0 5%
76.5 14. 2%
31.8 11..0%
33.3 118.7%
52.3 14.00%
49.2 19.0%
+nnermAL+GenFT
73.1.15.8%
80.610.798
33.3
33.3 11..
59.012.9%
55.0p1 .8%
Original
88.7
91.8
64.7
56.1
92.2
77.4
+RefInject
63.7.28.
73.7_ 10. 198
73.7_10.79
52.3
11..11.220 29%
46,8 0%
84.818.0%
71.2 18.0%
Qwen3-8B
-RefInjesttGenFT
68. (8.1122..55 123..5%
18.0_16..55
55.4 (14.4%
49.1 49.1412.0%
15.2
74.2
+InterAL
74.1g16.4%
83.419.2%
55.6114.0%
49.4
87.218.4%
73.5
+ +hieemAL+GenFT
76.2
85.4
55.6
51.0
89.612.8%
75.4 22.0%
Original
89.0
92.8
68.6
65.6
83..3
75.3
+RefInject
63.3
65,5 .2..%%
59.6
59.3
80.0
74.7
Qwen3-32B
+ +Relnnjec+GenFT
63.9.28
69.3
4%%
60.2.12.3%
60.2
81.5 1%%
76.3
+ +ItermAL
70.5
73.2.21.25.
63.9 13.916.8%
59.7,9.056
81.4
74.610.8%
+ GenFT
76.3_14.3%
10.0
64.1
59.1
83,9
77.1
Relative Performance (9)
Deval
Relatiive Performance (9)
MedQA
Relative Performance (96)
MMLU-Med
100
1000
100
80
80
80
2000
4000
6000
8000
0
2000
4000
6000
8000
D
2000
4000
6000
8000
# Injection Samples
# Injection Samples
# Injection Samples
Relative Performance (%)
MMLU-O
Rel Relative Performance (%)
ARC-C
Rel atlvve Performance (%)
CSQA
100
1.00
100
80
80
80
0
2000
4000
6000
8000
0
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# Injection Samples
# Injection Samples
# Injection Samples
Refinject
RefeT
InternAL
IreFM
Figure 10:
Relative performance (%) of Qwen3-8B trained with different knowledge injection
methods on various evaluation benchmarks, with varying numbers of injected knowledge triples. All results normalized to the model's performance prior to injection.
Internal-aware Sample Augmentation As introduced in Section 4.1, we augment the knowledge injection process with the internal knowledge by adding the extracted tail entities as correct answers to the injection examples and convert the original multiple-choice question into multiple-answer question. Specifically, for each triplet (h,, r,,t,) tt) for injection, we first retrieve the corresponding internal knowledge k((b).rr)
h2 r Tı, (h, r, E Kinmer). Then the maximum number of correct options is set to n max = max( (I<: (hs.)r)) + 1,4). We limit the maximum number of correct options to 4 to ensure the difficulty of the question.
[h,r t)]h
'nnerr
Then, we conduct uniform sampling to select the number of correct options n Uniform[1, n m=x)
(bbrr))
and randomly sampling 1 tail entities from K to combine with the original tail entity t;
28

---

Table 10: Question templates used for probing and extracting the related internal knowledge.
Relation Type
Probing Tomplate
Extraction Template
protein-interact with-proein
What genes or proteins are involved in protein-protein interactions with the protein [bead]]
Given the paragraph below, extract all the proteins that are involed protein-protein interactions with "[head]".
drug has carrier protein
What proteins carry drug [head]?
Given the paragraph beluw, extract all proteins that carry the drug "[head]"
drug-has enzyme-protein
What proteins metabolize the drug [head]?
Given the paragraph below, extract all the proteins that are enzymes of the drug "[head]"
drug-has target-pr prget-protein
What proteins targeted by the drug
Given the paragraph below, extract all the proteins that are largeted by the drug "[head]".
[head]]
drug-hm has transporter-protcit
What proteins transport the drug [head]]
Given the paragraph below, extract all the proteins that transport the drug '[hcad]". Given the paragraph below, extract all the diseases that contraindicated by the drug "[head]'.
drug-has contraindiiationndissee
What diseases are contraindicated by the drug [head]]
drug has indication disease
are indications for the drug
Given the paragraph below, extract all disenses that are indicated by the drug "[head]".
[head]?
drug-has off-label use-disease
What diseases treated off-label by the drug [head]]
Given the paragraph below, extract all the discases that are treated off label by the drug "[head]".
drug-interact with-drug
What drugs have a drug-drug interaction with [hcad]]
Given the paragraph below, extract all the drugs that have a drug-drug interaction with the drug "[head]'.
protein-associate witb-phenottye pe
What effects phenotypes are associated with [Bead]]
Given the paragraph below, extract all efllcts/pphnollye that are associated with protein "[head]" :
discase-phenen pisense-phenottpe wreenttphenotyyp
What phenotypes are present in the discase
Given the paragraph below, extract all the phenotypes that are present in the disease "[eedd]"
[head]]
protein-a associated with disease
What diseases are associated with [head]]
Given the paragraph below, extract all the diseases that are associated with the gene/protein "[head]".
drug-side effect-effect
What side effects are caused the drug [bead]?
Given the paragraph below, extract all the side effects of the drug "[head]"
prolein-interacts mulecular function
What molecular functions associated with [head]?
Given the paragraph below, extract all molecular functions that the gene/protein "[head]" interacts with.
protein-interacts with-cellular component
What cellular components interact with [head]?
Given the paragraph below, extract all the cellular components that the gene/protein "[head]' interacts with.
protein-interacts with biological process
What biological processes interact with [head]?
Given the paragraph below, extract all biological processes that the gene/protein "[head]" interacts with.
exposure-interacts with-protein
What genes proteins interact with the exposure of [head]]
Given the paragraph below, extract all the proteins that interact with the exposure of "[head]".
exposure-linked to-discase
What diseases are linked to the exposure of [head]?
Given the paragraph below, extract all the diseases are linked to the exposure of "[head]".
exposure-interacts with-bbological process
What biological processes interact with the exposure of [head]?
Given the paragraph below, extract
biological processes that the exposure of "[head]" interacts with.
protein interacts with pathway
What pathways does [head] involive in?
Given the paragraph below, extract all the pathways that the gene/protein "[head]" in- volves in.
protein-expression present in-anatomy
What anatomical locations show expression
Given the paragraph below, extract all the anatomical locations that the protein "[head]" is expressed in.
of [heud]?
as the correct options. The distractors are randomly sampled from the Prime dataset. The final injection example is then generated by filling the head entity h; relation r;, and the selected correct options into the template as follows:
For a list of medical entities A:.., B: ..... C: ... ... I.., J: .... among the given list, [injection reference] include option [list of answers].
In this way, we can augment the knowledge injection process with the related internal knowledge, avoiding the catastrophic forgetting of the knowledge that is closely related to the injected knowledge.
29

---

I Hallucination-Level Analysis
Though the augmented knowledge used in the proposed method may contain some noise, it is generated by the target model prior to injection, meaning that its hallucination level is inherently bounded by that of the model, with no external noise introduced. To validate this, we selected five relation types in PrimeKG and, for each, randomly chose five head entities. We then prompted the model with open-ended questions to generate tail entities and measured precision through manual evaluation. We compare the precision of the original model and that of the model after applying InternAL, as shown in Table IT Experimental results show that the model trained with InternAL achieves higher precision, suggesting that the proposed approach not only avoids amplifying hallucinations, but may even help reduce them. We speculate that this may be because hallucinations in the original model that contradict the newly injected knowledge are partially suppressed during the injection process, thereby reducing the overall hallucination level.
Table 11:
Precision (%) of the original model and the model after applying InternAL. on the generated tail entities for selected relation types in PrimeKG.
Precision
Original
InternAL (ours)
drug-has iiicaaton-diseass
47.7
90.0
protein-interact with-bit iith-biological process
45.3
64.0
disease-phe pheessephenotype prssent-phenotypp
83.3
84.7
drug-side effect-effect
87.6
92.4
exposure-linke to-disease
49.6
59.5
Total
62.7
78.1
Generalizability to Other Domains
Though the proposed InternAL method is primarily designed for medical knowledge injection, has the potential to be generalized to other domains. To verify this, we further conducted an addi- tional small-scale study beyond the medical field. Specifically, we selected human geography as the target domain and extracted all sister city relationships from Wikidata (i.e., long-term partnerships between cities established through official agreements), sampling 20,000 city pairs for experimen- tation. Following the same methodology used in the paper, we constructed evaluation questions to identify a subset of knowledge that was poorly mastered by the model (6,857 pairs selected for injection, denoted as Kajpcit ), and a well-mastered subset with model accuracy over 75% (4,145
pairs selected for evaluating forgetting, denoted as DSisciy), We then applied both the baseline method (RefInject) and our proposed method (InternAL) for knowledge injection. For evaluation, leverage sister-city- test sets DSSiCay and DSisCity as well on suite of general-domain benchmarks. Furthermore, to evaluate the model's forgetting of domain- but semantically distant knowledge, we constructed an additional test set, CityLoc, by generating 7,174 questions based the latitude and longitude information of cities extracted from Wikidata. We also studied the effect of seneral-domate finetuning (GenFT), an effective approach for mitigating catastrophic forgetting in the general domain.
Experiments are conducted based on Llama3-8B, and the results are provided in Table 12 results above show that (1) direct knowledge injection (RefInject) leads to 25% forgetting rate DSisCity and 5.4% forgetting rate on CityLoc, while general-domale finetuning (GenFT) fails to effectively address the substantial forgetting on Disciity and CityLoc; (2) Our method (InternAL) significantly mitigates forgetting on DSSaciiy (from 64.3 to 82.0) and on CityLoc (from 67.2 to 72.6). This demonstrates that our method can effectively reduce catastrophic forgetting in other domains beyond medicine, especially for knowledge that is closely related to the injected knowledge.
The
30

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Table 12:
Performance (%) of LLMs on human geography benchmarks after injecting knowledge using the baseline and proposed methods.
Model
D
SiaCrtt Inject
D SisCsty
CityLoc
Llama3-8B
9.1
89.3
72.6
+RefInject
93.9
64.3
67.2
RefInject+GenFF
93.0
69.0
66.8
+nttrrAL (ours)
93.9
82.0
72.6
+ntermAL+GenFT
94.5
83.9
72.1
K
Generalizability to Other Data Formats
Though the proposed method InterAAL is primarily designed for the injection structured medical knowledge, it can also be generalized to unstructured knowledge formats, such clinical guidelines. To verify this, we conducted an additional small-scale study using clinical guidefines as the injection knowledge. Specifically,
randomly sampled 2,000 clinical guidelines from publicly available dataset [37], and used GPT-4.1 to generate multiple-chor questions (MCQs) for each guideline. subset of these questions We used these MCQs to evaluate the performance of LLaMA3-SB and selected 185 guidelines with accuracy below 50% as the injection knowledge set (Kinjet)). and 1,542 guidelines with accuracy above 75% as the evaluation set (Dova) to monitor forgetting. We adopt continued pretraining (CPT) as our approach for knowledge injection. Given the limited amount of injection data, we utilize commonly used data augmentation techniques, generating multiple paraphrased versions of the training samples in order to enhance the diversity of injection. Built on that, we further extend our proposed method (IntemAL to the unstructured knowledge. Specifically, we first extract key medical entities from each training sample using the target LLM,, then prompt the model recall relevant knowledge associated with these entities, and finally integrate the recalled knowledge into training samples to construct enriched pretraining texts.
manually reviewed and found be largely reliable for evaluation.
The evaluation results are summarized in Table We observed the following phenomena from the results: (I) Continued Pretraining (CPT) achieves considerable performance on tasks related the injected knowledge, but leads to sigmificant forgetting, which exhibits proximity-depende forgetting characteristics (a drop of 7.8 Deval. an average decrease of 6.3 on medical benchmarks, and an average decrease of 4.2 on general datasets); (3) Incorporating the internal relevant knowledge of LLMs into the training data (InternAL.) can effectively mitigate forgetting, especially on the medical evaluation sets.
Table 13: Performance (%) of LLMs on medical and general benchmarks after injecting knowledge the form of unstructured text using different methods.
Medical
General
Method
Dinject
D Deval
MedQA
MMLU-Med MMLU-O ARC-C CSQA
Llama3-8B
29.7
93.7
50.7
69.8
59,8
75,4
66.4
+CPT
49.6
85.9
44.3
63.6
55.8
72.1
61.0
Relative Forgetring
8.3
12.6
8.9
6.7
4.4
8.1
+InternAL (ours)
47.0
87.8
46.0
65.8
56.7
72.4
62.5
Relative Forgetting
6.3
9.3
5.7
5.2
4.0
5.9
31


Reviews:
[{"review_id": "l9AFRgjcbS", "reviewer": "Reviewer_w9Ko", "summary": "This paper investigates catastrophic forgetting in LLMs when injecting medical knowledge via fine-tuning. The authors reveal a proximity-dependent pattern of forgetting: knowledge semantically close to the injected content is more susceptible to being overwritten, while unrelated knowledge is largely unaffected. The study empirically assesses several existing mitigation techniques and finds their performance lacking in the medical setting, particularly for knowledge close to injected facts. Inspired by mechanisms of human learning, the authors introduce InternAL, which augments the injection dataset by probing the LLM for its own closely related internal knowledge and incorporating these outputs into fine-tuning. Experiments with LLama3-8B and Qwen3-8B across a range of medical and general benchmarks demonstrate that InternAL significantly reduces proximity-based forgetting while maintaining injection effectiveness.", "questions": "Check the weakness part mentioned above.", "limitations": "Check the weakness part mentioned above.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\nWell-Motivated Problem Formulation: The paper addresses an under-explored and practically pertinent issue: catastrophic forgetting during domain-specific knowledge injection in LLMs, with a focus on the medical domain.\nPractical solution : InternAL is simple yet effective, leveraging the model’s own knowledge to mitigate forgetting.\n\nWeaknesses:\nLimited scope : Only tested in medical domain; other domains might behave differently.\nModel size limitation : Experiments only cover 8B-parameter models (LLama, Qwen). Larger (e.g., 30B) or smaller (e.g., 1B) models may exhibit different forgetting dynamics, but this is untested.", "was_revised": true, "final_justification": "I am satified with the author's response.", "rebuttal": "We sincerely appreciate your constructive feedback. Below, we address each of the concerns you raised.\n\n1. **Research scope issue**: We sincerely appreciate your insightful comments. In fact, our focus on the medical domain is primarily motivated by a key observation: current LLMs still lack the necessary medical knowledge to perform reliably in real-world clinical scenarios. Our work investigates the phenomenon of catastrophic forgetting caused by medical knowledge injection and proposes an internal knowledge augmentation learning method to mitigate this issue, thereby improving the effectiveness of medical knowledge injection. Consequently, the majority of our experiments are conducted in the medical domain, which we also stated in the *Limitation* section.\n\n   Nevertheless, considering the underlying mechanisms of knowledge injection, the proximal-dependent forgetting pattern identified in this work may also exist in other domains. Based on your constructive suggestion, we conducted an additional small-scale study beyond the medical field. Specifically, we selected **human geography** as the target domain and extracted all **sister city relationships** from Wikidata (i.e., long-term partnerships between cities established through official agreements), sampling 20,000 city pairs for experimentation.\n\n   Following the same methodology used in our paper, we constructed evaluation questions to identify a subset of knowledge that was poorly mastered by the model (6,857 pairs selected for injection, denoted as $K_{\\text{inject}}^{\\text{SisCity}}$), and a well-mastered subset with model accuracy over 75% (4,145 pairs selected for evaluating forgetting, denoted as $D_{\\text{eval}}^{\\text{SisCity}}$). We then applied both the baseline method (**RefInject**) and our proposed method (**InternAL**) for knowledge injection. For evaluation, we leverage sister-city-based test sets $D_{\\text{inject}}^{\\text{SisCity}}$ and $D_{\\text{eval}}^{\\text{SisCity}}$ as well as on a suite of general-domain benchmarks. Furthermore, to evaluate the model’s forgetting of domain-related but semantically distant knowledge, we constructed an additional test set, *CityLoc*, by generating 7,174 questions based on the latitude and longitude information of cities extracted from Wikidata. We also studied the effect of general-domain finetuning (GenFT), an effective approach for mitigating catastrophic forgetting in the general domain. Experiments are conducted based on Llama3-8B, and the results are as follows:\n   |Model|$D_{\\text{inject}}^{SisCity}$(Injected)|$D_{\\text{eval}}^{SisCity}$ (Uninjected)|CityLoc|\n   |-|-|-|-|\n   |Original (Llama3-8B)|9.1|89.3|72.6|\n   |RefInject|93.9|64.3|67.2|\n   |RefInject+GenFT|93.0|69.0|66.8|\n   |**InternAL (ours)**|93.9|82.0|72.6|\n   |**InternAL**+GenFT|94.5|83.9|72.1|\n\n   The results above show that (1) direct knowledge injection (RefInject) leads to a 25% forgetting rate on $D_{\\text{eval}}^{\\text{SisCity}}$ and a 5.4% forgetting rate on CityLoc, while general-domain finetuning (GenFT)  fails to effectively address the substantial forgetting on $D_{\\text{eval}}^{\\text{SisCity}}$ and CityLoc; (2) Our method (InternAL) significantly mitigates forgetting on $D_{\\text{eval}}^{\\text{SisCity}}$ (from 64.3% to 82.0%) and on CityLoc (from 67.2% to 72.6%). Once again, thank you for your valuable feedback, and we will include this experiment in the revised version to support the generalizability of our main findings.\n\n2. **Model size issue**: Thank you for your constructive suggestions. We selected the LLaMA and Qwen model families for our experiments primarily because they are representative open-source models with strong performance on several medical-domain benchmarks [1–2]. Considering the clinical applicability of LLMs, we primarily focus on the 8B model size, which offers a practical trade-off between computational resources, deployment cost, response latency, and reliability in real-world clinical environments. Larger models are often impractical to deploy due to their high resource demands and slower inference speed, while smaller models may lack sufficient medical knowledge to ensure reliable performance. \n\n   Indeed, models of different sizes may exhibit distinct forgetting dynamics. Nevertheless, our preliminary experiments also revealed similar forgetting patterns across different model sizes. Motivated by your constructive comment, we further conducted additional experiments on Qwen3-1.7B (full-parameter finetune) and Qwen3-32B (using LoRA due to limited computational resources). The results are as follows:\n\n   | Qwen3-1.7B          | $D_{\\text{inject}}$ (Injected) | $D_{\\text{eval}}$ (Uninjected) | MedQA | MMLU-Med | MMLU-O | ARC-C | CSQA |\n   | ------------------- | ---------------------------------------- | ---------------------------------------- | ----- | -------- | ------ | ----- | ---- |\n   | Original            | 9.7                                      | 88.7                                     | 37.5  | 59.0     | 52.5   | 71.6  | 66.4 |\n   | RefInject           | 61.3                                     | 66.2                                     | 29.2  | 48.2     | 46.0   | 59.9  | 53.9 |\n   | RefInject+GenFT     | 60.4                                     | 72.8                                     | 31.3  | 55.0     | 52.1   | 68.1  | 63.3 |\n   | **InternAL (ours)** | 59.3                                     | 75.1                                     | 31.9  | 51.7     | 47.4   | 61.7  | 58.3 |\n   | **InternAL**+GenFT  | 58.7                                     | 79.2                                     | 33.3  | 58.0     | 52.0   | 68.5  | 61.8 |\n\n   | Qwen3-32B           | $D_{\\text{inject}}$ (Injected) | $D_{\\text{eval}}$ (Uninjected) | MedQA | MMLU-Med | MMLU-O | ARC-C | CSQA |\n   | ------------------- | ---------------------------------------- | ---------------------------------------- | ----- | -------- | ------ | ----- | ---- |\n   | Original            | 10.2                                     | 92.4                                     | 68.2  | 80.8     | 68.5   | 86.9  | 83.5 |\n   | RefInject           | 71.1                                     | 62.7                                     | 59.1  | 77.6     | 67.6   | 84.7  | 84.4 |\n   | RefInject+GenFT     | 73.6                                     | 68.6                                     | 60.3  | 80.1     | 70.7   | 89.0  | 84.1 |\n   | **InternAL (ours)** | 64.7                                     | 72.8                                     | 62.5  | 79.6     | 67.9   | 86.7  | 82.7 |\n   | **InternAL**+GenFT  | 67.7                                     | 83.1                                     | 63.2  | 81.8     | 72.5   | 90.0  | 83.0 |\n\nExperimental findings are summarized as follows:\n\n1. Both Qwen3-1.7B and Qwen3-32B exhibit **proximity-dependent forgetting**: For Qwen3-1.7B, knowledge injection (RefInject) led to performance degradation across multiple evaluation sets. While general-domain finetuning (GenFT) effectively mitigated forgetting in the general domain, the forgetting in the medical domain remained substantial. In contrast, Qwen3-32B showed relatively minor forgetting in the general domain after RefInject, but more pronounced forgetting in the medical domain (e.g., MedQA dropped from 68.2 to 59.1), which could not be effectively mitigated by GenFT.\n2. Our proposed method, **InternAL**, effectively alleviates catastrophic forgetting in the medical domain for both models. Furthermore, it can be combined with GenFT to achieve even better mitigation of forgetting.\n\nWe sincerely thank the reviewer for the insightful suggestion. We will include this experiment in the revised version of the paper to better support the observed forgetting patterns and the generalizability of our proposed method.\n\n[1] Wu C, Qiu P, Liu J, et al. Towards evaluating and building versatile large language models for medicine. npj Digital Medicine, 2025.\n\n[2] Zhu S, Hu W, Yang Z, Yan J, Zhang F. Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study. JMIR Med Inform. 2025."}, {"review_id": "386Wue6XRc", "reviewer": "Reviewer_gLAz", "summary": "This paper focusing on solving the catastrophic forgetting problem in LLMs happend during knowledge injection in medical domain. Firstly, the authors argue that they observed that LLMs tend to forgetting knowledge related to the new knowledge that will be injected. Based on this discovery, they propose a method that will first probe the knowledge which closely related to the injecting knowledge. Then, they use the extracted knowledge to augment the knowledge materials to help the LLMs to retain the information.", "questions": "1. Can you briefly why are you choosing the RefInject method to do the knowledge injection? Is it possible that the knowledge relativeness pattern you observed will only happen for this injection method?\n2. Did I miss something? Why the models performance dropped on EVERy datasets after injection? I expected some improvement on specific dataset after injection (Otherwise, what is the point of injection?)\n3. L197-198, why are the numbers in your lines are different from the numbers in the table?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. This paper presents a clear and practically meaningful objective: addressing the problem of catastrophic forgetting in LLMs during the process of knowledge injection. The authors highlight an intriguing observation—LLMs tend to forget pre-existing knowledge that is closely related to the newly injected content. To mitigate this issue, the paper proposes a simple yet effective solution, offering valuable insights into the continual learning dynamics of LLMs.\n2. To evaluate the effects of knowledge forgetting and injection, the authors design a comprehensive and well-structured experimental setup. They partition the knowledge data into two sets: one with low factual accuracy, used for the knowledge injection experiments, and another with high factual accuracy, employed to assess the extent of knowledge forgetting.\n3. This work is well-illustrated. The author first use enough experiments to show their claim holds. Then introduce their proposed method in a expectable manner. Readers will accept their proposed method more smoothly.\n\nWeaknesses:\n1. One potential concern I have with this work is that the authors’ definition of “related knowledge” appears somewhat overly broad. For instance, in some parts of the paper, all knowledge within the medical domain is treated as related, while in specific experiments, a distinction is made between proximal and distal knowledge. This inconsistency may affect the clarity and interpretability of the findings.\n2. A second concern lies in the generalizability of the proposed approach. While the problem identified by the authors is indeed broadly relevant, the experiments are conducted on a relatively structured and constrained setting—primarily focusing on knowledge represented in the form of triples. However, in many real-world scenarios, knowledge does not naturally conform to such structured formats. It remains unclear how the proposed method would extend to more unstructured or complex forms of knowledge representation.", "was_revised": true, "final_justification": "I am satisfied with the rebuttal from the author since it adress most of my concern. While this paper reveal an interesting observation for LLM forgetting mechanism and verified by well-designed experiments. But the proposed solution is not very advanced. Therefore, I recommend to accept this paper for the sake of encouraging more continuing research on this topic. So, I will maintain my origin rating as 5, accept.", "rebuttal": "We sincerely appreciate your kind and constructive feedback as well as your recognition of our work. Below are our responses to each of the concerns you raised.\n\n1. **Clarification of related knowledge**: Thank you for your careful reading and thoughtful comments. Actually, the “related knowledge” we discuss in the paper is a gradable concept, which can be roughly categorized into three levels:\n\n   - **Level 1**: Knowledge within the medical domain that is semantically highly related to the injected knowledge (*proximal*);\n   - **Level 2**: Knowledge within the medical domain that is less semantically related (*distal*);\n   - **Level 3**: Knowledge outside the medical domain.\n\n   Our experimental findings reveal that the catastrophic forgetting caused by medical knowledge injection becomes progressively less severe from Level 1 to Level 3, exhibiting a **proximal-dependent** pattern. We sincerely apologize for not articulating this point clearly in the paper. We will revise the relevant definition in the updated version to clarify this concept.\n\n   1. A second concern lies in the generalizability of the proposed approach. While the problem identified by the authors is indeed broadly relevant, the experiments are conducted on a relatively structured and constrained setting—primarily focusing on knowledge represented in the form of triples. However, in many real-world scenarios, knowledge does not naturally conform to such structured formats. It remains unclear how the proposed method would extend to more unstructured or complex forms of knowledge representation.\n\n2. **Generalizability issue**: We sincerely appreciate your insightful comments. In this work, we primarily focus on structured medical knowledge primarily because structured knowledge bases (e.g., DrugBank, UMLS) are abundant in the medical domain and represent one of the fundamental forms of medical knowledge. Moreover, the nature of structured knowledge allows for more precise measurement of semantic proximity, aiding the analysis of the forgetting pattern. That said, we agree that in real-world applications, knowledge also appears in unstructured forms such as free-text clinical narratives. Based on your constructive feedback, we conducted a preliminarily study to explore forgetting behaviors when injecting unstructured knowledge and to validate the effectiveness of our proposed method in mitigating such forgetting. \n\n   Specifically, we randomly sampled 2,000 clinical guidelines from a publicly available dataset [1], and used GPT-4.1 to generate 5 multiple-choice questions (MCQs) for each guideline. A subset of these questions was manually reviewed and found to be largely reliable for evaluation. We used these MCQs to evaluate the performance of LLaMA3-8B and selected 185 guidelines with accuracy below 50% as the injection knowledge set ($K_{\\text{inject}}$), and 1,542 guidelines with accuracy above 75% as the evaluation set ($D_{\\text{Eval}}$) to monitor forgetting. We adopt continued pretraining (**CPT**) as our approach for knowledge injection. Given the limited amount of injection data, we utilize commonly used data augmentation techniques [2,3], generating multiple paraphrased versions of the training samples in order to enhance the diversity of injection. Built on that, we further extend our proposed method (**InternAL**) to the unstructured knowledge. Specifically, we first extract key medical entities from each training sample using the target LLM, then prompt the model to recall relevant knowledge associated with these entities, and finally integrate the recalled knowledge into training samples to construct enriched pretraining texts. The evaluation results are summarized below:\n\n| Method                | $D_{\\text{inject}}$ (Injected) | $D_{\\text{eval}}$ (Uninjected) | MedQA   | MMLU-Med | MMLU-O  | ARC-C   | CSQA    |\n| --------------------- | -- | ---------------------------------------- | ------- | -------- | ------- | ------- | ------- |\n| Original              | 29.7  | 93.7  | 50.7    | 69.8     | 59.8    | 75.4    | 66.4    |\n| CPT                   | 49.6 | 85.9 | 44.3    | 63.6     | 55.8    | 72.1    | 61.0    |\n| *Relative Forgetting* | -| 8.3 | 12.6    | 8.9      | 6.7     | 4.4     | 8.1     |\n| **InternAL (ours)**   | 47.0 | 87.8 | 46.0    | 65.8     | 56.7    | 72.4    | 62.5    |\n| *Relative Forgetting* | -  | **6.3**  | **9.3** | **5.7**  | **5.2** | **4.0** | **5.9** |\n\nWe observed the following phenomena from the results: (1) Continued Pretraining (CPT) achieves considerable performance on tasks related to the injected knowledge, but leads to significant forgetting, which exhibits proximity-dependent forgetting characteristics (a drop of 7.8 on $D_{\\text{eval}}$, an average decrease of 6.3 on medical benchmarks, and an average decrease of 4.2 on general datasets); (3) Incorporating the internal relevant knowledge of LLMs into the training data (InternAL) can effectively mitigate forgetting, especially on the medical evaluation sets.\n\nWe sincerely appreciate your suggestion and will include these additional experiments and analyses in the revised paper to discuss the generalizability of our findings and methods to unstructured knowledge.\n\n3. **Reason for choosing RefInject**: Thank you for your thoughtful comment. We chose the RefInject method for knowledge injection for the following reasons: (1) Several prior studies in the medical domain [1,4,5] have demonstrated that training on MCQ datasets can effectively inject medical knowledge into LLMs and improve their performance on downstream medical tasks. (2) Other work has pointed out that continued pretraining on limited text data may fail to achieve effective generalization [6], whereas using corpora that contain implicit associations between entities (e.g., MCQ) has been shown to facilitate more efficient knowledge learning [2]. Motivated by these findings, we developed the RefInject method, which constructs MCQ-like questions that encode implicit associations between entities in the injected knowledge, aiming to enhance injection efficiency. Nevertheless, the proximity-dependent forgetting pattern we observed is **not** unique to this injection method. As mentioned in our previous response, we also observed this pattern when injecting knowledge through continued pretraining on unstructured clinical guidelines. We will further refine the relevant discussion in the revised manuscript to better clarify our motivation for choosing RefInject.\n\n4. **Clarification of the experimental results**: We are very grateful for your thorough reading. The primary goal of knowledge injection is to supplement the missing knowledge in LLMs and improve their performance on tasks related to the injected knowledge. In this work, we randomly sampled 20,864 knowledge triples from PrimeKG and identified a subset where the LLMs performed poorly as the target knowledge for injection. After the injection, the model showed significant improvement on evaluation tasks corresponding to the injected knowledge (e.g., 9.7 to 77.4 for RefInject on $D_{\\text{inject}}$). Although some forgetting occurred on the remaining triples, the overall performance across all 20,864 knowledge triples is improved by the injection (as shown in the $D_{\\text{total}}$ column of the table below):\n\n| Model                | $D_{\\text{inject}}$ | $D_{\\text{eval}}$ | $D_{\\text{total}}$ |\n| -------------------- | ----------------------------- | --------------------------- | ---------------------------- |\n| Original (Llama3-8B) | 9.7 | **91.4** | 51.5  |\n| RefInject            | **77.4** | 60.3 | 65.0 |\n| RefInject+GenFT      | 73.4  | 71.4 | 68.8  |\n| **InternAL (ours)**  | 74.3 | 70.9 | 69.3|\n| **InternAL+GenFT**   | 71.4 | 77.4 | **71.2**|\n\nThe results above demonstrate that the baseline injection method (RefInject) improves performance on all knowledge triples by 13.5% compared to the original model. In contrast, our proposed method (RefInject) further alleviates forgetting and achieves an additional 4.3% improvement across all triples, while also effectively mitigating forgetting on external benchmarks. \n\nThank you again for your kind comments. We will further clarify the relevant statements and add the total performance $D_{\\text{total}}$ in the revised version to clarify the purpose and effectiveness of knowledge injection.\n\n5. **Number Mismatch Issue**: Thank you for your careful reading. In fact, the results in lines 197–198 were from our initial single-run experiments. To verify robustness, we later conducted two additional independent runs and updated the table to report the average over all three runs, which showed consistent trends. However, we overlooked updating the corresponding description in the text. We apologize for the oversight and will correct this in the revised manuscript.\n\n6. **Paper Formatting Concerns**: Thank you for your careful reading and helpful comment regarding Tables 2 and 3. We will address this in the revised version to improve clarity and presentation.\n\n[1] Chen Z, Cano A H, Romanou A, et al. Meditron-70b: Scaling medical pretraining for large language models. arXiv preprint arXiv:2311.16079, 2023.\n\n[2] Cabezudo M A S, Inácio M L, Pardo T A S. Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation. Proceedings of the 17th International Natural Language Generation Conference. 2024.\n\n[3] Zhang X, Li M, Wu J. Co-occurrence is not factual association in language models. NeurIPS 2024.\n\n[4] Singhal, K., Tu, T., Gottweis, J. *et al.* Toward expert-level medical question answering with large language models. Nature Medicine, 2025. \n\n[5] Han T, Adams L C, Papaioannou J M, et al. MedAlpaca--an open-source collection of medical conversational AI models and training data[J]. arXiv preprint, 2023.\n\n[6] Christopher Potts, and Danqi Chen. Mquake: Assessing knowledge editing in language models via multi-hop questions. EMNLP 2023."}, {"review_id": "ilrUe12Ijr", "reviewer": "Reviewer_ze9w", "summary": "This paper investigates the problem of catastrophic forgetting in large language models (LLMs) during domain-specific knowledge injection, focusing on the medical domain. The authors identify a proximity-dependent forgetting phenomenon, where knowledge that is semantically close to the injected content is more likely to be forgotten. To address this, they propose InternAL, a novel method that leverages the LLM’s own internal knowledge to augment the injection data.", "questions": "How is the MEMIT method adapted to the multiple-choice question format used for knowledge injection in this paper?", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n1.  The proposed InternAL method is simple and easy to implement, yet achieves strong empirical performance in mitigating catastrophic forgetting.\n\n2.  The paper provides a valuable analysis revealing a proximity-dependent forgetting pattern.\n\n\n**Weakness**\n\n1.  One limitation of this work lies in its reliance on multiple-choice questions to construct the training data. This results in a constrained learning format that may fail to reflect the diverse forms in which knowledge is typically conveyed. In real-world scenarios, knowledge injection should ideally leverage free-form text, such as medical textbooks or clinical narratives. Similarly, evaluation should incorporate a broader range of question types to better assess the model’s generalization ability.\n\n2.  While InternAL demonstrates empirical effectiveness in mitigating proximity-dependent forgetting, its underlying mechanism remains relatively simple. Fundamentally, InternAL can be seen as a data augmentation strategy that enriches the training set by incorporating prior knowledge closely related to the injection targets. It shares conceptual similarities with replay-based methods.\n\n3.  InternAL relies on self-generated knowledge without explicit quality control, which may introduce noise or reinforce hallucinated content during training.\n\n4.  The paper does not provide a representation-level analysis to explain how InternAL preserves semantically proximal knowledge, leaving its internal mechanisms somewhat opaque.", "was_revised": true, "final_justification": "I am inclined to maintain my score: while the authors’ response addressed some of my concerns, the paper’s data construction still constrains the method’s generalizability.", "rebuttal": "We are very grateful for your kind and constructive feedback. Below are our responses to each of the concerns you raised.\n\n1. **Knowledge form issue**: We sincerely thank the reviewer for the insightful feedback. Our method primarily leverages multiple-choice questions (MCQs) for knowledge injection, following prior work [1–3], as MCQs are effective for conveying medical knowledge during SFT. We focus on structured knowledge (e.g., DrugBank, UMLS) due to its abundance in the medical domain and its utility for precisely computing semantic proximity, which facilitates analysis of forgetting.\n\nWe also agree that free-form texts such as medical textbooks and clinical narratives also contain rich medical knowledge and can be utilized for knowledge injection. In response to your suggestion, we conducted a preliminary study to assess the generalizability of our method to free-form texts. We sampled 2,000 clinical guidelines from a public dataset [3] and used GPT-4.1 to generate five MCQs per guideline. After manual review of a subset, we found the questions reliable for evaluation. We then evaluated LLaMA3-8B on these questions and selected 185 low-performing guidelines (accuracy < 50%) as the injection set ($K_{\\text{inject}}$), and 1,542 high-performing ones (accuracy > 75%) as the evaluation set ($D_{\\text{Eval}}$). For knowledge injection, we applied continued pretraining (**CPT**). Given the limited size of the injection corpus, we adopted a common data augmentation technique [4,5] that generates paraphrased variants of the injection samples to improve data diversity.\n\n   Building on this, we further extended our method (**InternAL**) to the unstructured knowledge setting. Specifically, we first prompted the LLM to extract key medical entities from each training sample. We then retrieved relevant knowledge associated with these entities by prompting the LLM again, and finally integrated this knowledge into the original samples to produce enriched pretraining texts. The evaluation results for this preliminary study are summarized as follows:\n\n   | Method | $D_{\\text{inject}}$ (Injected) | $D_{\\text{eval}}$ (Uninjected) | MedQA   | MMLU-Med | MMLU-O  | ARC-C   | CSQA|\n   | - | -- | - | - | -- | - | - | - |\n   | Original| 29.7  | 93.7  | 50.7| 69.8| 59.8| 75.4| 66.4|\n   | CPT| 49.6  | 85.9  | 44.3| 63.6| 55.8| 72.1| 61.0|\n   | *Relative Forgetting* | -| 8.3   | 12.6| 8.9 | 6.7| 4.4| 8.1|\n   | **InternAL (ours)**   | 47.0  | 87.8  | 46.0| 65.8| 56.7| 72.4| 62.5|\n   | *Relative Forgetting* | -| **6.3**| **9.3** | **5.7**  | **5.2** | **4.0** | **5.9** |\n\n   We observed the following key findings from the results: (1) Continued Pretraining (CPT) leads to substantial performance gains on tasks related to the injected knowledge but also causes significant forgetting that exhibits a proximity-dependent pattern (a 7.8-point drop on $D_{\\text{eval}}$, an average decrease of 6.3 on medical benchmarks, and 4.2 on general datasets); (2) Our method (**InternAL**) mitigates forgetting effectively by integrating relevant internal knowledge into the training data, yielding notable improvements especially on the medical evaluation sets.\n\n   Furthermore, based on your constructive suggestions, we additionally constructed fill-in-the-blank (FIB) questions using GPT-4.1 based on clinical guidelines to investigate the effectiveness of knowledge injection across different question types. Specifically, we generated medical statements from the guidelines and then removed key information, prompting the evaluated model to fill in the missing content. We used GPT-4.1 to verify the correctness of the model’s answers by comparing them to the ground truth. The experimental results are shown below. (Here, $D_{\\text{inject}}^{\\text{mcq}}$ and $D_{\\text{eval}}^{\\text{mcq}}$ refer to multiple-choice questions generated from the injected knowledge and uninjected knowledge, respectively, while $D_{\\text{inject}}^{\\text{fib}}$ and $D_{\\text{eval}}^{\\text{fib}}$ refer to fill-in-the-blank questions generated from the injected knowledge and uninjected knowledge, respectively.)\n\n   | Method| $D_{\\text{inject}}^{\\text{mcq}}$ | $D_{\\text{eval}}^{\\text{mcq}}$ | $D_{\\text{inject}}^{\\text{fib}}$ | $D_{\\text{eval}}^{\\text{fib}}$ |\n   | --- | -- | -- | - | -- |\n   | Original  | 29.7  | 93.7| 35.1  | 40.8|\n   | CPT  | 49.6  | 85.9| 52.4  | 35.0|\n   | **InternAL (ours)** | 47.0  | 87.8| 53.0  | 35.8|\n\n   Our experiments show that the proposed InternAL method can also improve the injection efficiency of the model on fill-in-the-blank questions and mitigate forgetting to some extent. These preliminary results further support that the proximity-dependent forgetting pattern is not confined to the specific knowledge types and training data examined in our study, and that the proposed method exhibit potential for extension to unstructured training data. We sincerely thank you again for your constructive suggestions, and we will further refine this experiment and analysis in the revised manuscript.\n\n2. **Difference from replay-based methods**：Thank you for the thoughtful feedback. While both our InternAL method and replay-based approaches address catastrophic forgetting from a data perspective, they differ in two key ways: (1) Replay methods rely on access to prior training data, whereas our method extracts relevant internal knowledge from the LLM itself, requiring no past data; (2) Replay involves joint training with old and new data, increasing data volume and cost. In contrast, our method enhances the injection data without increasing its size, keeping training efficient. We will elaborate on these differences in the Related Work section.\n\n3. **Hallucination level issue**: Thanks for your feedback. Our goal is to enhance knowledge injection and reduce catastrophic forgetting by extracting relevant knowledge from the target LLM itself. Since the augmented knowledge is generated by the model prior to injection, its hallucination level is inherently bounded by that of the model, with no external noise introduced. To validate this, we selected five relation types and, for each, randomly chose five head entities. We then prompted the model with open-ended questions to generate tail entities and measured precision through manual evaluation. Results were compared between the original LLaMA3-8B and the model trained with our method (InternAL) as follows:\n\n   | Precision| Original | InternAL (ours) |\n   | -- | - | -- |\n   | drug-has  indication-disease| 47.7| **90.0**   |\n   | protein-interacts  with-biological process | 45.3| **64.0**   |\n   | disease-phenotype present-phenotype   | 83.3| **84.7**   |\n   | drug-side effect-effect| 87.6| **92.4**   |\n   | exposure-linked  to-disease | 49.6| **59.5**   |\n   | Total   | 62.7| **78.1**   |\n\n   Experimental results show that the model trained with our method achieves higher precision, suggesting that our approach not only avoids amplifying hallucinations, but may even help reduce them. We speculate that this may be because hallucinations in the original model that contradict the newly injected knowledge are partially suppressed during the injection process, thereby reducing the overall hallucination level. We sincerely appreciate your feedback and will further refine this analysis and incorporate it into the revised manuscript.\n\n4. **Representation-level analysis**: Thank you for the constructive suggestion. To analyze how InternAL preserves semantically proximal knowledge, we encoded non-injected knowledge  ($D_{\\text{eval}}$) statements from PrimeKG using three models: the original LLaMA3-8B, RefInject, and InternAL. We extracted representation vectors across layers and computed the cosine distance between the original model and the two injected models. The difference in these distances reveals how much each method alters the original representations. Results are as follows: (we omitted some layers due to limited space)\n\n   | Layer | Cosine Distance of RefInject (%) | Cosine Distance of InternAL (ours) (%) | $\\Delta$ |\n   | ----- | - | -- | -------- |\n   | 1| 1.5| 1.5 | 0   |\n   |...|...|...| ...|\n   | 6| 16.5| 11.1| 5.4 |\n   | 7| 18.9| 12.3| 6.6 |\n   | 8| 20.1| 13.6| 6.5 |\n   | 9| 22.1| 15.2| 6.9 |\n   | 10| 23.5| 16.1| 7.4 |\n   | 11| 24.8| 17.8| 7   |\n   | 12| 25.5| 19.5| 6   |\n   | ...| ...| ...| ...|\n   | 32| 14.6| 14  | 0.6 |\n\nExperimental results show that InternAL has a smaller impact on non-injected knowledge representations than RefInject, which may result in less forgetting. Notably, the difference in cosine distance is significantly larger across layer 6-12 than other layers, which is consistent with prior findings on factual knowledge localization [5,6]. We will include this analysis, along with a t-SNE visualization, in the revised paper to further support the theoretical explanation.\n\n5. **MEMIT with MCQ format**: Thank you for the suggestion. We initially followed prior work [6,7] using statement-style prompts for MEMIT. Based on your feedback, we began testing the MCQ format, but due to the high cost (∼2 days for injection), results are pending and will be shared later.\n\n[1] Singhal  et al. Toward expert-level medical question answering with large language models. Nature Medicine, 2025. \n\n[2] Han et al. MedAlpaca--an open-source collection of medical conversational AI models and training data[J]. arXiv preprint, 2023.\n\n[3] Chen et al. Meditron-70b: Scaling medical pretraining for large language models. arXiv preprint arXiv:2311.16079, 2023.\n\n[4] Cabezudo M A S, Inácio M L, Pardo T A S. Investigating Paraphrase Generation as a Data Augmentation Strategy for Low-Resource AMR-to-Text Generation. Proceedings of the 17th International Natural Language Generation Conference. 2024.\n\n[5] Zhang et al.. Co-occurrence is not factual association in language models. NeurIPS 2024.\n\n[6] Meng et al. Locating and editing factual associations in gpt. NeurIPS 2022.\n\n[7] Meng et al. Mass-Editing Memory in a Transformer. ICLR 2023."}, {"review_id": "xwc1HpjQJA", "reviewer": "Reviewer_AipE", "summary": "This paper investigates catastrophic forgetting in Large Language Models (LLMs) during domain-specific fine-tuning, particularly for medical knowledge injection. The authors identify a \"proximity-dependent\" forgetting pattern, where knowledge semantically close to the newly injected content is most likely to be forgotten. Finding existing mitigation techniques insufficient, they propose a novel method called InternAL (Internal Knowledge Augmentation Learning). This approach probes the LLM for its own internal knowledge related to the facts being injected and then uses this retrieved knowledge to augment the fine-tuning dataset. By co-training on both the new knowledge and related existing knowledge, InternAL significantly mitigates catastrophic forgetting, especially for closely related concepts, while maintaining strong performance on the injected knowledge. The method's effectiveness is demonstrated on multiple LLMs, such as LLaMA and Qwen.", "questions": "Q1.Could the authors provide an analysis of the quality of the knowledge extracted by the f_probe module? I suggest conducting a small-scale manual evaluation on a random sample.\n\nQ2. Could the authors please provide more specific details on the negative sampling strategy used in the RefInject method? Specifically, are the m-1 negative tail entities sampled uniformly at random from the entire PrimeKG knowledge graph, or are they \"hard negatives\" (e.g., entities of the same type as the true answer, or entities that frequently co-occur with the head entity)?", "limitations": "No potential negative societal impact.", "rating": 4, "confidence": 1, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper addresses the critical and highly practical problem of catastrophic forgetting during domain-specific fine-tuning of LLMs. This is particularly significant in high-stakes domains like medicine, where retaining broad knowledge while incorporating new, specialized facts is essential for safe and effective real-world application. \n2. It leverages the model's own internal knowledge, probed and extracted based on the new knowledge being injected, to create a rehearsal dataset. This is a creative and resource-efficient solution inspired by human learning\n\nWeaknesses:\n It is unclear how these findings on proximity-dependent forgetting would translate to other knowledge-intensive domains like law, finance, or even more general commonsense reasoning.", "was_revised": false, "final_justification": "", "rebuttal": "We are sincerely grateful for your kind and constructive feedback. Below are our responses to each of the concerns you raised.\n\n1. **Generalizability to other domain**: Thank you for the insightful feedback. Our emphasis on the medical domain stems from a central observation: current LLMs often lack sufficient medical knowledge to support reliable decision-making in real-world clinical settings. To address this, our work investigates the issue of catastrophic forgetting that arises from medical knowledge injection and introduces an internal knowledge augmentation method, *InternAL*, to mitigate this problem and enhance injection effectiveness. As such, most of our experiments are conducted within the medical domain, as also acknowledged in the *Limitation* section. Nevertheless, the forgetting pattern we identify—proximity-dependent forgetting—may not be unique to the medical domain. Motivated by your comments, we extended our study to a different knowledge domain: **human geography**. Specifically, we extracted all **sister city relationships** from Wikidata (i.e., long-term partnerships between cities formalized via official agreements) and sampled 20,000 city pairs for analysis.\n\n   Using the same methodology as in our main study, we created evaluation questions to determine which knowledge was poorly mastered (6,857 pairs selected for injection, denoted as $K_{\\text{inject}}^{\\text{SisCity}}$) and which was already well-mastered (4,145 pairs with model accuracy >75%, selected for evaluating forgetting, denoted as $D_{\\text{eval}}^{\\text{SisCity}}$). We then conducted knowledge injection using both the baseline (**RefInject**) and our proposed method (**InternAL**). Evaluation was performed on $D_{\\text{inject}}^{\\text{SisCity}}$(injected knowledge), $D_{\\text{eval}}^{\\text{SisCity}}$(uninjected knowledge), and several general-domain benchmarks.\n\n   To further assess forgetting of related but semantically distant knowledge, we created an additional test set, *CityLoc*, containing 7,174 questions based on city latitude and longitude data from Wikidata. We also explored the effect of general-domain finetuning (**GenFT**), a known strategy for reducing catastrophic forgetting in broader domains. All experiments were conducted using Llama3-8B. The results are summarized as follows:\n\n| Model                | $D_{\\text{inject}}^{SisCity}$(Injected) | $D_{\\text{eval}}^{SisCity}$ (Uninjected) | CityLoc |\n| -------------------- | ------------------------------------------------- | -------------------------------------------------- | ------- |\n| Original (Llama3-8B) | 9.1                                               | 89.3                                               | 72.6    |\n| RefInject            | 93.9                                              | 64.3                                               | 67.2    |\n| RefInject+GenFT      | 93.0                                              | 69.0                                               | 66.8    |\n| **InternAL (ours)**  | 93.9                                              | 82.0                                               | 72.6    |\n| **InternAL**+GenFT   | 94.5                                              | 83.9                                               | 72.1    |\n\nThe results demonstrate that (1) direct knowledge injection (RefInject) leads to a 25% forgetting rate on $D_{\\text{eval}}^{\\text{SisCity}}$ and a 5.4% forgetting rate on CityLoc, while general-domain finetuning (GenFT)  fails to effectively mitigate the substantial forgetting on $D_{\\text{eval}}^{\\text{SisCity}}$ and CityLoc; (2) Our method (**InternAL**) significantly mitigates forgetting on $D_{\\text{eval}}^{\\text{SisCity}}$ (from 64.3% to 82.0%) and on CityLoc (from 67.2% to 72.6%). We will include this experiment in the revised version to support the generalizability of our main findings.\n\n2. **Quality of extracted knowledge**: Thanks for your constructive feedback. Due to the inherent hallucination tendencies of large language models, the knowledge extracted using the *f_probe* module may inevitably contain a certain degree of inaccuracy. However, since the extracted knowledge reflects the hallucination level of the target LLM itself, using this knowledge for internal augmentation learning does not amplify the model’s hallucination tendencies. Based on your helpful suggestion, we selected five relation types. For each relation, we randomly chose five head entities involved in the knowledge injection process. We then generated open-ended questions to prompt the model to produce tail entities that form the specified relation with the given head entity. The precision of the generated tail entities was used as the evaluation metric, and we conducted manual evaluation on both the original LLaMA3-8B model and the model trained using our proposed method (InternAL). The results are as follows:\n\n   | Precision                                  | Original | InternAL (ours) |\n   | ------------------------------------------ | -------- | --------------- |\n   | drug-has  indication-disease               | 47.7     | **90.0**        |\n   | protein-interacts  with-biological process | 45.3     | **64.0**        |\n   | disease-phenotype present-phenotype        | 83.3     | **84.7**        |\n   | drug-side effect-effect                    | 87.6     | **92.4**        |\n   | exposure-linked  to-disease                | 49.6     | **59.5**        |\n   | Total                                      | 62.7     | **78.1**        |\n\n   Experimental results indicate that the model trained using our approach results in higher precision, implying that our method not only avoids reinforcing hallucinations but may actually contribute to mitigating them. We hypothesize that this effect arises because hallucinated content in the original model that conflicts with the newly injected knowledge is partially suppressed during the injection process, leading to an overall reduction in hallucination. We greatly appreciate your feedback and will further refine this analysis in the revised version of the manuscript.\n\n3. **Details of the negative sampling strategy**: We sincerely appreciate your careful reading. The negative sampling strategy we implemented indeed corresponds to what you referred to as “hard negatives.” Specifically, based on each knowledge triple to be injected, we first extract all entities from PrimeKG that share the same type as the tail entity. We then exclude any entities that already form the specified relation with the given head entity, and randomly sample from the remaining pool to generate negative options. In our experiments, we found that this strategy yields training samples that enable efficient knowledge injection and sufficiently support our study. We will revise the manuscript to clarify this detail accordingly."}]

Author rebuttal:

Meta-review: This paper tackles the important and practical problem of catastrophic forgetting in LLMs during domain-specific knowledge injection, with a focus on the medical field.

The paper's primary contribution is twofold. First, it provides a solid analysis that identifies and empirically validates a "proximity-dependent" forgetting phenomenon, where knowledge semantically close to the newly injected content is more likely to be forgotten. Second, it proposes InternAL, a simple yet effective method that mitigates this specific type of forgetting. The core idea of leveraging the model's own internal knowledge to create a rehearsal dataset for augmentation is both novel and resource-efficient.

The experimental analysis is thorough, and the authors have done an excellent job addressing reviewer concerns during the rebuttal period by providing additional experiments that strengthen their claims regarding generalizability across different domains, model sizes, and data formats.

The findings presented here offer valuable insights into the mechanisms of knowledge retention in LLMs and open a promising avenue for future research. I encourage the authors to continue their work in this important direction. Therefore, I recommend this paper for acceptance.
Decision: Accept (poster)