Rationale-Enhanced Language Models are Better Continual Relation Learners
Authors: Weimin Xiong
Yifan Song
Peiyi Wang
Sujian Li
Venue: EMNLP 2023 Main (2023)
Keywords: continual learning
relation extraction
rationale

Abstract: Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous relations. To address the issue, we introduce rationale, i.e., the explanations of relation classification results generated by Large Language Models (LLM), into CRE task. Specifically, we design the multi-task rationale tuning strategy to help the model learn current relations robustly. We also conduct contrastive rationale replay to further distinguish analogous relations. Experimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models.

Rationale-Enhanee Language Models are Better Continual Relation Learners
Weimin Xiong Yifan Song Peiyi Wang Sujian Li*
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University (wmxiong, , yfsong, iisujian]@pkuu edu. cn
wangpeiyig979@gmall 9979@gmail. com
Abstract
Question: is the fourth installment in the "Predator" film series, set to take place between " "Predator 2" (1990) and
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning sequence of newly emerging relations. Recent CRE studies have found that catastrophic forgetting arises from the model's lack of robustness against future analogous rela- tions. address issue, we introduce ratio- nale, i.c., the explanations of relation classifica- tion results generated by large language models (LLLM), into CRE task. Specifically, we de- sign the tulti-takk rationale tuning strategy to help the model learn current relations robustly. We also conduct contrastive rationale replay to further distinguish analogous relations. Ex- perimental results on two standard benchmarks demonstrate that our method outperforms the state-of-the-art CRE models. Our code is avail- able https: //github..ttt com/ hoom/Weimnnee /ttp//iiiiiion//////// n RationaleCL
"Predators" (2010). Classify the relation in above sentence.
Answer: follow by
The relation between "Predator 2" and "Predators" is "followed by". Please explain why.
Prompt:
The relation between "Predatorr 2" (1990) and Preddtorr" (2200) is "followed by" bocause "Predator 2" the second installment of the "Predator" " film series, and it is followed by the fourth installment, which "Predators".
Rationale:
Similar relations: follows; followed by; characters
Prompt: The relation between "redatoor 2" and "Predators" is "followed by", instead of "follows" or "characters", Please explain why.
Contrastive Rationale: sentence states that "Predater took place before "Predators", so the proper relationship to denote this would be "followed by". The phrase "follows" suggests that "Predator 2" took place after "Predators" The phrase "characters" has no relation to the two films, as refers to the people movie.
Figure I: Examples of the input question text and the ra- tionale and contrastive rationale for relation answer.
1 Introduction
restricted gradient updating and knowledge distil- lation (Lopez-Paz and Ranzato, 2017; Cao et al., 2020). Recently, another line of work (Wang al., 2022b) found that in CRE scenario, models trained for the current task do have good identification ability for new-coming relations which are analo- gous to a current relation. Thus, making a model learn current relations robustly to avoid subsequent confusion becomes the new research focus (Wang et al., 2022b; Zhao et al., 2023).
Relation extraction (RE) aims to identify the rela- tions between two entities in a text. While tradi- tional RE models cannot handle the real-life sit- uation where new relations are constantly emerg- ing, continual relation extraction (CRE) attempts to learn new relations while retaining the performance on learned relations (Han et al., 2020; Cui al., 2021; Zhao et al., 2022). Similar to other contin- ual learning tasks, the main challenge in CRE is the phenomenon of catastrophic forgetting (CF), i.e., the performance on identifying old relations degrades significantly while learning new relations.
To address this problem, in this paper, we as- sume that incorporating rationales can enhance the performance of CRE models in learning analogous relations. This is inspired by the intuition that, training models with explicit rationale supervision can provide greater robustness (Chen et al., 2022). Moreover, since relation extraction requires reason- ing over two entities, providing explanations for why the two entities have a specific relation can enhance the reasoning capacity of smaller models,
Most previous CRE researches have attributed catastrophic forgetting the destruction of repre- sentations learned on previous tasks when learning new tasks (Han et al., 2020; Cui et al., 2021; Wang et al., 2022a). They focused on recovering the rep- resentations on previous tasks, using methods like
"Corresponding Author

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(a) Framework of RationaleCI.
(b) Contrastive rationale generation
Figure 2: An overall demonstration of our proposed RationaleCL.
thereby eliminating reliance on spurious shortcuts (Li et al., 2022; Magister et al., 2023). As there are no such corpus which labels the entity relations with corresponding rationales, we propose to make use of LLM to generate the explanations, i.e., the rationales, for the relation classification answers with prompting questions, as shown in Figure 1.
Task Formalization
In this work, focus on continual learning for sequence of relation classification tasks (Ti, T2,..., Tn}. For task Tre given a training set Dx = {( w/) E Xg. yi E Yı}, the model learns to identify the corresponding relation type Yi for the input text a;, where Y2 denotes a set of new relation types and X a set of text contain- ing two entities. The goal of CRE is to contin- ually train the model new tasks to learn new relations, while avoiding forgetting of previously learned ones. Therefore, after learning n lasks, the model will be evaluated to identify each text x from test set into a relation type y E L 1
To fully exploit rationales, we propose novel approach called RationaleCI, which incorporates two strategies: multi-task rationale tuning and con- trastive rationale replay, into likearsal-based framework, as shown Figure Specifically, we employ the encoder-decoder model T5 (Raffel et al., 2020) as our backbone, utilizing multi-task rationale tuning with three tasks: question to an- swer the main task, question to rationale-answer and question-rationall to answer as auxiliary tasks. With the rationale tuning strategy, distill the rationale knowledge from LLM make T5 de- velop the reasoning ability interpret its classifica- tion results, leading to enhanced robustness for the CRE task. When conducting memory rehearsal, we prompt LLM to differentiate between analogous relations and regenerate the corresponding explana- tions, i.c., the contrastive rationales (Figure 1), to update the memory. which not only helps mitigate catastrophic forgetting but prevents confusion.
3 Methodology
Figure 2 shows the model architecture of Ra- tionalCL, which follows two-stage training paradigm. In Stage 1, we first make use of LLM to generate rationales, with which the T5 model is trained under the multi-task rationale tuning frame- work learn to identify new relations. Then, alleviate catastrophic forgetting CRE, we adopt an episodic memory module to store a few repre- sentative instances for each relation. The mem- ory data will be continually learned with emerging new tasks. When selecting these representative instances for each relation, we follow Han et al. (2020) and use the K-means algorithm to conduct clustering based on the features of all samples ex- tracted by T5-encoder, and select the samples in the centre of each cluster into memory. Stage 2, we regenerate the contrastive rationale and conduct contrastive rationale replay under the multi-task framework to alleviate catastrophic forgetting.
Our contributions are summarized as follows: (1) For the first time, we introduce rationale gen- erated by LLM into CRE task to mitigate catas- trophic forgetting. (2) We propose novel rationale- enhanced CRE method RationaleCL, which incor- porates multi-task rationale tuning and contrastive rationale replay strategies. (3) Experimental results on Few Rel and TACRED verify the effectiveness our method.
3.1 Rationale Generation
Previous researches have illustrated the capability of LLM to decompose problems into series

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TACRED
Models
RPCRE (Cui al., 2021)
97.6
90.6
86.1
82.4
79.8
77.2
75.1
73.7
72.4
72.4
EMAR (Han et al., 2020)
97.8
92.4
89.6
84.6
83.2
81.3
78.7
77.1
77.3
76.8
CRECL (Hu et al., 2022)
97.3
90.5
86.1
84.6
82.1
79.4
77.6
77.9
CRL (Zhao et al., 2022)
97.7
93.2
89.8
84.7
84.1
81.3
80.2
79.1
79.0
78.0
ACA (Wang et al., 2022b)
92.1
84.4
78.1
CEAR (Zhao et al., 2023)
97.7
94.3
92.3
88.4
86.6
84.5
82.2
81.1
80.1
79.1
RationaleCL.
98.6
94.4
91.5
88.1
86.5
84.9
84.5
82.5
81.6
80.8
Few Rel
Models
T1
T2
T3
T4
T5
T6
T7
T8
T9
T10
RPCRE (Cui et ul., 2021)
97.9
92.7
91.6
89.2
88.4
86.8
85.1
84.1
82.2
81.5
EMAR (Han ul., 2020)
98.2
94.1
92.0
90.8
89.7
88.1
87.2
86.1
84.8
83.6
CRECL (Hu et al., 2022)
98.0
94.7
92.4
90.7
89.4
87.1
85.9
85.0
84.0
82.1
CRL (Zhao al., 2022)
98.1
94.6
92.5
90.5
89.4
87.9
86.9
85.6
84.5
83.1
ACA (Wang et al., 2022b)
98.3
95.0
92.6
91.3
90.4
89.2
87.6
87.0
86.3
84.7
CEAR (Zhao et al., 2023)
98.1
95.8
93.6
91.9
91.1
89.4
88.1
86.9
85.6
84.2
RationaleCL
98.6
95.7
93.4
92.3
91.3
89.7
88.2
87.3
86.3
85.1
Table 1:
Accuracy (%) on seen relations after learning each task. We show the best results in boldface.
intermediate reasoning steps' (Wei et al., 2023; Kojima et al., 2023). Therefore, leverage this capability of LLM to produce explanation, i.c., rationale, for the relation classification datasets. Specifically, as shown in Figure 1, for each instance (m4, vi) in the training set, we construct the corre- sponding prompt and let the LLM (gpt-3.5-ture in this paper) generate the rationale r; explaining why æ; is identified as the relation type 2
classification results. Task,, which is similar to Chain-of- reasoning, instructs the model to predict the relation classification result y and generate the corresponding rationale r; why ii is predicted from the question 24. This task helps the model grasp the real rationale and thereby avoid dependency on the spurious shortcut. To enable the model make full use of the rationale for relation prediction, Task, is designed to take and r; as input and produce a relation result
3.2 Multi-task Rationale Tuning
The training objective of our model is combi- nation of the losses for the three tasks above:
Given an instance (Iz; yi) E D2 and its rationale ri, we define three tasks: question to answer (Taske), question to rationale-answer (Task,,) and question- rationale to answer (Taska) as follows:
f aLe + a)(3,
()),
where and B are factors used to adjust the loss weights.
Taske Ií + y/i
Task, 24 + rá + 34
3.3 Contrastive Rationale Replay In the replay stage, the model has access mem- ory from all previously learned relations. To fur- ther reduce confusion over analogous relations, we prompt LLM to regenerate the contrastive rationale that distinguishes them, and update memory with the new rationale. The process of contrastive ra- tionale generation is depicted in Figure 2(b). As shown, first find the analogous relations for each relation. Following Wang et al. (2022b), we em- ploy the cosine distance of the average embedding of the instances as a metric to measure the simi- larity of two relations. We consider two relations
Taska 2,
34
Figure 2(a) illustrates the multi-task rationale tuning strategy. Task, is the main task, which let the model directly generate the relation y; for the input question text o;. To enhance the robust- ness of the model for future analogous relations, we design two auxiliary tasks Task, and Taska to help develop its reasoning ability to interpret its
prob- lem involving numerous relations on its own without finetun- ing. Please refer Appendix for more detail. WWo save the generated rationale for re-using.
'However. LLM faces difficulties tackling

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90
TI0, RationaleCL improves the accuracy of SOTA methods CEAR/ACA by 1.7/2.7 and 0.9/0.4 on TACRED and FewRel, respectively. These results demonstrate the effectiveness and universality our proposed method.
Filri
wi con. caplay
spore
80
75
4.3 Analysis
70
subsidiary sibling
spouse
follows
father
ITAR
Figure 3: Performance on analogous relations. "mt" de- notes multi-task rationale tuning. "con. replay" denotes contrastive rationale replay.
TACRED
are similar if their similarity exceeds a threshold т. For each instance (Ti; Di, Tz) stored in memory, we identify the similar relations of and create new prompt to regenerate its contrastive rationale cr; shown in Figure 1. The contrastive rationale cr, is intended highlight the distinctions between the similar relations and is used to replace r; in memory. Subsequently, the model trained on the updated memory.
11
(6) Memory size
(6) Manory size
Figure Comparison of model's performance on dif- ferent memory sizes.
Improvements on Analogous Relations We fur- ther analyse the ability of the model to distinguish between analogous relations. We show the per- formance of relations which has larger similarity to other relations, as mentioned by (Wang et al., 2022b). As shown in Figure 3, the model's Fl scores those relations are improved greatly after using multi-task rationale tuning and further en- hanced with contrastive rationale replay. We show some cases Appendix D.
4 Experiments
4.1 Experimental Settings
Following previous work (Han et al., 2020; Cui et al., 2021; Hu et al., 2022; Wang et al., 2022a), our experiments are conducted upon two widely-used datasets, FewRel (Han et al., 2018) and TACRED (Zhang al., 2017). Please refer to Appendix A.1 for more details.
Datasets
Influence of Memory Size Memory size is de- fined the number of stored typical samples for each relation, which is a key factor for the model performance of rehearsal-based CRE meth- ods. Therefore, we study the influence of memory size on our method. As shown in Figure 4, our method outperforms ACA and CEAR with mem- ory sizes and 15, demonstrating that our model is robust to the change of memory size.
Implementation Details To ensure fair compar- isons, we adhere to the identical experimental set- ting employed by (Wang et al., 2022a). We ran- domly divide all relations into 10 subsets corre- sponding to 10 tasks, and accuracy all observed relations is chosen as the evaluation metric. The random seeds are identical to guarantee that the task sequences remain the same. We maintain a fixed number of 10 stored instances in memory for each relation and report the average result of five different task sequences. More details of our ex- perimental settings and comparison baselines are included in Appendix A.2 and A.3.
Ablation Study We further conduct ablation study of our proposed two strategies. The experi- mental results Table 2 show a performance degra- dation with the ablation of both strategies, demon- strating the effectiveness of our proposed rationale- enhanced framework. For more ablation results, please refer to Appendix C.
4.2
Main Results
5 Conclusion
The performances of our proposed RationaleCL and baselines on two datasets are shown Table 1. Our method consistently outperforms all baselines with significance pest < 0.05. For the accuracy of
In this paper, we devise a framework by introduc- ing rationales generated by LLM into CRE tasks to improve the model's robustness against future

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Models
TACRED
FewRel
References
RationaleCI.
80.8
85.1
Pengfei Cao, Yubo Chen, Jun Zhao, and Taifeng Wang. 2020. Incremental event detection via knowledge consolidation networks. In Proceedings of the 2020 Conference on Empirical Methods in Natural Lan- guage Processing (EMNLP), pages 707-717, Online. Association for Computational Linguisties.
w/o con. replay
80.2
84.8
w/o Task.
80.0
84.6
w/o both
79.8
83,9
Table 2: Ablation study results. "con. replay" denotes contrastive rationale replay.
Howard Chen, Jacqueline He, Karthik Narasimhan, and Danqi Chen. 2022. Can rationalization improve ro- bustness? In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies. pages 3792-3805, Seattle, United States. Association for Computational Linguistics.
analogous relations. Our method incorporates two strategies to help the model effectively learn current relations and better differentiate between analogous relations. Experimental results on two benchmarks show that our method consistently outperforms pre- vious state-of-the-art CRE models. Further anal- ysis confirms the effectiveness of our proposed rationale-enhanced framework.
Li Cui, Deqing Yang, Jiaxin Yu, Chengwei Hu, Jiayang Cheng, Jingjic Yi, and Yanghua Xiao. 2021. Refin- ing sample embeddings with relation prototypes to enhance continual relation extraction. In Proceed- ings of the 59th Annual Meeting of the Association for Computational Linguistics and the I I1th International Joint Conference (Volume 1: Long Papers), pages 232-243 Online. Association for Computational Linguistics.
Limitations
Natural Language Processing
Our paper has several limitations: 1) Our method uses manually designed prompts to generate ra- tionales. However, the choices of prompts may have a great impact on the quality of rationales, which has not been investigated. 2) Our method suffers from efficiency problems. On the one hand, the multi-task rationale tuning strategy increases GPU memory consumption and introduces extra computational overhead. On the other hand, the generation of contrastive rationale needs to be car- ried out repetitively, increasing the consumption of calling LLM API. 3) While our method de- veloped for the CRE task, it can also be applied to other continual learning tasks, which will be a focus of our future work.
Ridong Han, Tao Peng, Chaohao Yang, Benyou Wang. Lu Liu, and Xiang Wan. 2023. Is information extrac- tion solved by chatgpt? an analysis of performance, evaluation criteria, robustness and errors. arXiv preprint arXiv:2305. 14450.
Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2020. Contin- ual relation learning via episodic memory activation and reconsolidation. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, pages 6429 6440, Online. Association for Computational Linguistics.
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao. Zhiyuan Liu, and Maosong Sun. 2018. FewRel: A large-scale supervised few-shot relation classification dataset with statc-of-the-aa evaluation. In Proceed- ings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4803-4809, Brussels, Belgium. Association for Computational Linguistics.
Ethics Statement
Our work complies with the ACL Ethics Policy. Since relation classification is a standard task in NLP and all datasets we used are publicly avail- able, we have not identified any significant ethical considerations associated with our work.
Chengwei Hu, Deqing Yang, Haoliang Jin, Zhen Chen, and Yanghua Xiao. 2022. Improving continual rela- tion extraction through prototypical contrastive lear- ing. In Proceedings of the 29th International Con- ference on Computational Linguistics, pages 1885- 1895, Gyeongju. Republic of Korea, International Committee on Computational Linguistics
Acknowledgement
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa. 2023. Large lan- guage models are zero-shot reasoners.
We thank the anonymous reviewers for their helpful comments this paper. This work was partially supported by National Key R&D Program of China (No. 2022YFC3600002) and National Social Sci- ence Foundation Project China (21&ZD287).
Bo Li. Gexiang Fang. Yang Yang, Quansen Wang. Wei Ye, Wen Zhao, and Shikun Zhang. 2023. Evaluating chatgpt's 's information extraction capabilities: An as- sessment of performance, explainability, calibration. and faithfulness. arXiv preprint arXiv:2304. 1633.

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Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng. Yi Mao, Wenhu Chen, and Xifeng Yan. 2022. Explanations from large language models make small reasoners better.
David Lopez-Paz and Marc' Aurelio Ranzato. 2017. Gradient episodic memory for continual learning.
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksci Severyn. 2023.
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Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang. and William Yang Wang. 2019. Sen- tence embedding alignment for lifelong relation ex- traction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguisticx: Human Language Tech- nologies, Volume I (Long and Short Papers), pages 796-806, Minneapolis, Minnesota. Association for Computational Linguistics.
Peiyi Wang, Yifan Song, Tianyu Liu, Rundong Gao, Binghuai Lin, Yunbo Cao, and Zhifang Sui. 2022a. Less is more: Rethinking state-of-thur continual relation extraction models with frustratingly easy but effective approach.
Peiyi Wang, Yifan Song, Tianyu Liu, Binghuai Lin, Yunbo Cao, Sujian Li, and Zhifang Sui. 2022b. Learning robust representations for continual rela- tion extraction via adversarial class augmentation. In Proceedings of the 2022 Conference on Empiri- cal Methods in Natural Language Processing, pages 6264-6278, Abu Dhabi, United Arab Emirates. As- sociation for Computational Linguistics.
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A Experimental Details
Datasets
Test set sizes
Accuracy
FewRel TACRED
11200 1240
38.71
A.1 Datasets
54.84
Following previous work (Han et al., 2020; Cui et 2021; Hu et al., 2022; Wang et al., 2022a), our experiments are conducted upon the following two standard benchmarks with the train-test-validttion split ratio 3:1:1.
Table 3: Performance of LLM in FewRel and TACRED.
RPCRE (Cui et al., 2021) proposes relation prototypes and memory network refine sample embeddings, which effectively retains the learned representations in CRE.
FewRel (Han et al., 2018) a RE benckmark dataset originally proposed for fhw-shot learning. The dataset contains 100 relations, each with 700 instances. Following the previous work (Wang et al., 2019; Han et al., 2020), we use the origi- nal training and validation set of FewRel, which contains 80 relations.
CRL (Zhao et al., 2022) proposes to utilize contrastive learning and knowledge distilla- tion to alleviate catastrophic forgetting.
CRECL (Hu et al., 2022) introduces proto- typical contrastive learning to ensure that data distributions of all CRE tasks are more distin- guishable to alleviate catastrophic forgetting.
TACRED (Zhang et al., 2017) It is large- scale dataset containing relations (including no relation) and 106,264 samples, which is con- structed on news networks and online documents. Following (Cui et al., 2021), no pe_relaaioon was re- moved in our experiments, and the number of train- ing samples for each relation is limited to 320 and the number of test samples of each relation to 40.
ACA (Wang et al., 2022b) designs two adver- sarial class augmentation mechanisms learn robust representations to alleviate catastrophic forgetting.
CEAR (Zhao et al., 2023) proposes memory- intensive relation prototypes and memory aug- mentation reduce overfitting typical sam- ples rehearsal stage.
A.2 Experimental Details
our experiment, we use T5-base-Ima cease-lm--daptt as our backbone model and Adam as our optimizer. We set the learning rate le-4 for the model. The batch size of training is 32 and 16 for FewRel and TACRED respectively. The memory size of each task is 10. The training epoch for stage and stage 2 are 10. Our experiments are conducted on single NVIDIA A800 GPU.
B Performance of LLM in RE tasks
As aforementioned in Section 3.1, although the LLM can generate a high-quality rationale for a given instance with its corresponding relation type, faces difficulties in tackling the relation classifi- cation problem involving numerous relations on its own without finetuning. In this part, we conduct experiments to show the performance of LLM (gpt- 3.5-turbo in this paper) in two RE tasks: FewRel and TACRED.
We find the best hyperparameter values through grid search with step of 0.1 for 3 and r. The search spaces for various hyperparameters are a E [0.4, 0.9). 3 E [0.4, 0.6] and E [0.95, 0.99]. The used hyperparameter values are listed below:
For FewRel, a 0.6, 6 0.5, 0.97.
We prompt LLM to generate the relation type y for an input text x in zero-shot setting and restrict to generate answers only within a given scope. We accomplish it by appending the set of relation types y to the input text æ and instructing LLM to select one best answer from Y. When answer outside Y is encountered, regenerating until an answer in the set Y is gener- ated. The number of relation types contained in the set is 80 and 40 corresponding to FewRel and TACRED, respectively.
For TACRED, a - 0.9, 8 - 0.5, t 0.97.
A.3 Baselines
We compare our proposed framework with the fol- lowing baselines in our experiments:
discard it and keep
EMAR (Han al., 2020) constructs mem- ory activation and reconsolidation mechanism to alleviate the catastrophic forgetting.

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Models
TACRED
FewRel
D
Case Study
RationaleCI.
80.8
85.1
In this section, we present several cases where the original model without rationale tuning makes in- correct predictions, while RationaleCL. correctly predicts the answer and the rationale, highlighting its robustness to analogous relations and avoidance of spurious shorteuts. These cases shown in Table 5.
w/o Task,-rr
80.6
84.9
w/o Taska-or
80.3
84.9
w/o Task & Task--or
80.2
84.8
Table More ablation study results.
As shown in Table 3, LLM performs poorly when prompted to directly generate the relation answer without finetuning. Therefore, we could not directly use LLM to perform RE tasks, we could exploit its strong reasoning ability to provide rationale for a correct relation answer.
The poor performance of ChatGPT on relation extraction tasks has also been verified several previous works (Li et al., 2023; Han et al., 2023). According to their studies, ChatGPT is limited by the output format requirements in accomplishing fine finegrrineed relation extraction tasks, and it is dif- ficult directly generate the target relation label within the defined range. However, ChatGPT's se- mantic understanding sufficient, and when we provide chatgpt with correct relation labels, Chat- GPT can understand the meaning of the relation according to the context and give reasonable ratio- nales. According to the maman-cheek results (Li et al., 2023), domain experts highly approve of the reasons given by ChatGPT.
C More Ablation Results
The purpose of contrastive replay is to produce contrastive rationale er; and replace rationale ry in memory, which is involved in Task, and Taska- As rú is replaced with cr; in both the tasks, there would be two "new" tasks for er; We denote them Task,-rr and Task In Task,-cr we only replace the rationale with cr; in Task, when conducting memory rehearsal while the opposite in Taska-cr. We provide further ablation analysis in Table 4.
Conclusively, the ablation study underscores the importance of both Taskd-cr and Taskr-er compo- nents in enhancing the performance of the Ratio- naleCL model. Their presence contributes to the model's robustness analogous relations, indicat- ing their roles in tackling the catastrophic forgetting challenges of continual learning effectively.

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Sentence with Instruction
Rationale Generated by Our Model
WA
RA
Given the subject entity "Lonely" and object entity 'Belly Dancer (Bananza)", what is the relation type between them in sentence: It spawned the singles "Locked Up" and "Lonely". "Belly Dancer (Bananza)". "Pot Of Gold", and "Ghetto"?
The relation between "Lonely" and "Blody Dancer (Banza)" is "followed by" because "Lonely" is mentioned first in the sentence, followed by "Blody Dancer (Banza)" in the sentence. This indicates that "Blody Dancer (Banza)" is one of the singles that came after "Lonely" in the genre of music.
follows
followed by
Given the subject entity "ABC" and ob- ject entity "Sydney", what is the relation type between them in sentence: Hitch- ener moved to the Nine Network after one year at ABC Sydney, presenting "National Nine News" first in Sydney and then in Melbourne as the presenter of "News Centre Nine"?
ABC Sydney is a television station that special- izes in broadcasting to the Sydney area. Hitch- ener moved to the Nine Network after one year at ABC Sydney, indicating that the station has its headquarters in Sydney. This indicates a rela- tionship between ABC and Sydney of "head- quarters location".
licensed to broadcust to
hcadquarters location
Given the subject entity "Bolshevik rev- olution" and object entity "Petrograd", what is the relation type between them in sentence: They were attempting to flee from their home in Petrograd (St Peters- burg) in Russia to escape the Bolshevik revolution, which Belevitch's father op- posed?
The relation between "Bolshevik revolution" and "Petrograd" in the sentence is "location". This is because the sentence states that the people were attempting to flee from their home in Pet- rSgrad(St Petersburg) in Russia to escape the Bolshevik revolution. The Bolshevik revolution took place in Petrograd, which is located in Rus- sia.
headquarters
location
location
Given the subject entity "Kronos Quar- tet" and object entity "Nonesuch", what is the relation type between them sen- tence: It was first recorded by guitarist Pat Metheny in 1987 and released to- gether with Reich's "Different Trains". performed by the Kronos Quartet, on Nonesuch's s 979 176 2?
The Kronos Quartet performed together with Reich's "Different Trains" on Nonesuch's 's 979 176-2, indicating that Nonesuch is the record label for the Kronos Quartet. This means that Nonesuch is the company responsible for dis- tributing and promoting the Kronos Quartet's music on the record lahel. Therefore, the answer is: record label.
performer
record label
Given the subject entity "Monkey Is- land" and object entity "LucasArts". what is the relation type between them in sentence: "DeathSpank" features ad- vanced dialog options, similar to Lu- casArts" "Monkey Island" scries which play a large part in unfolding the game's story?
LucasArts is the publisher of the "Monkey land" series of video games. It is responsible for the creation, promotion, and distribution of the series, making itself the publisher. Therefore. the answer is: publisher.
developer
publisher
Table 5: Case Study. "Sentence with Instruction" denotes the input question sentence. "Rationale Generated by Our Model" denotes the rationale generated by our aationale-enhancee model. "WA" denotes the wrong answer generated by model without rationale tuning. "RA" " denotes the right answer generated by our rationale-enhance model. We highlight the reasoning process in rationale in blue.


Reviews:
[{"review_id": "ky4w5feBJF", "reviewer": "Reviewer_hR9i", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your feedback and helpful suggestions on our work. We hope that the following response could clarify your main concerns.\n## Q1: How the significance test is performed？\nFirstly, we apologize for the typo \"p<0.5\" in the paper. In fact, we calculated p<0.05, but we incorrectly wrote p<0.5. Due to space limitation, we did not provide details of the significance test. We will refine the paper by adding the following details.\n\nWe use a significance test to determine whether the accuracy of our model is significantly higher than other models after learning the 10th task (T-test, p<0.05). The null hypothesis is that there would be no discrepancies. Specifically, FewRel-ACA: p=0.03; FewRel-CEAR:p=0.001; TACRED-ACA: p=0.002; TACRED-CEAR:p=0.009. Therefore, we can consider that our model significantly outperforms other baseline models. We will detail the significant test results in the final version.\n## Q2: The paper does not provide standard deviations or confidence intervals for any of its measurements.\nThanks for your question. We would like to provide some details as follows. \nFor the results in Table 1, on FewRel dataset, the standard deviation of our method RationaleCL is:\n\n|  Models      |T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | \n| ----------- | ----------- | ----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |\n| RationaleCL   |  0.46     | 1.27 | 1.67 | 1.53| 1.06 | 1.03 | 0.69 | 0.74 | 0.51 | 0.23 |\n\nFor the results in Table 1, on TACRED dataset, the standard deviation of our method RationaleCL is:\n\n|  Models      |T1 | T2 | T3 | T4 | T5 | T6 | T7 | T8 | T9 | T10 | \n| ----------- | ----------- | ----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |\n| RationaleCL   | 0.97    | 3.15 | 1.99 | 2.97| 2.60 | 1.71 | 1.76 | 1.35 | 0.43 | 0.72 |\n\nWe will provide the standard deviations results in the final version.\n## Q3: The ablation results look marginal, and are difficult to interpret without more statistical analysis.\nThanks for your question. We would like to provide some details for the significance test of the ablation results.\nThe null hypothesis is that there would be no discrepancies between the result of Rationale and other ablation settings and we report  p-value of Paired-Samples T Test.\n\n |  Models      | TACRED | FewRel |\n| ----------- | ----------- | ----------- |\n| w/o con. replay | 0.022 | 0.016 |\n|  w/o Taskd | 0.025 | 0.024 |\n|w/o both | 0.010 | 0.006 |\n\nWe believe these results (p<0.05) can demonstrate the effectiveness of our proposed rationale enhanced framework \n## Q4: How GPT-3.5 performed poorly on the tasks but was able to provide a useful supervision signal?\nThis is a good question. We will explain the reasons below. In fact, our RationaleCL is a multi-task learning model composed of three tasks, including question to answer as the main task, question to rationale-answer\nand question-rationale to answer as auxiliary tasks (see Section 3.2). In this architecture, the LLM-generated rationale  plays an important role in the  two auxiliary tasks to further promote the performance of the main task and the ablation experiments can reflect this (see Table 2.). \n\nThe poor performance of GPT-3.5 on relation extraction tasks has been verified in several previous work [1][2]. According to their studies, ChatGPT is limited by the output format requirements in accomplishing fine-grained relation extraction tasks, and it is difficult to directly generate the target relation label within the defined range. However, ChatGPT's semantic understanding is sufficient, and when we provide chatgpt with the correct relation labels, ChatGPT can understand the meaning of the relation according to the context and give reasonable rationales. According to the human-check results[1], **domain experts highly approve of the reasons given by ChatGPT**.\nHere are two cases in our experiment datasets.\n\n1. For a sentence in the TACRED dataset\n\nPolitical newcomer **andre carson** is the democratic nominee in a march 11 special election to succeed his late grandmother, **julia carson**, representing indiana's 7th district. \n\nThe  relation between \"julia carson\" and \"andre carson\" is \"person other family\". However, LLM predicts the relation to be \"person grandparents\", which is out of range.  Although the model predicts out-of-scope relations, it actually understands the meaning of the sentences and can give rationales.\n\n2. For a sentence in the FewRel dataset \n\nDuring the Crimean War, **Sir George Cathcart** held a dormant commission to take command of the **British Army** in Crimea if Lord Raglan was killed or disabled.\n\nThe relation between \"Sir George Cathcart\" and \"British Army\" is \"military branch\", but LLM predicts \"position held\". However, when given the label in the context, LLM provides reasonable rationale \"Sir George Cathcart was a high-ranking military officer and his commission was to take command of the British Army in Crimea, indicating that he was a part of the British Army's hierarchy and military branch.\"\n \nThanks again for the useful comments. We will continue to improve our paper according to your suggestions.\n\n[1] Evaluating ChatGPT's Information Extraction Capabilities: An Assessment of Performance, Explainability, Calibration, and Faithfulness. Arxiv\n\n[2] Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors. Arxiv", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- The approach proposed in the paper is interesting, and it is worth investigating whether LLM-generated reasoning can be used to supervise smaller models in this setting. \n\n- The method seems technically sound\n\n- The experiments are appropriate.", "reasons_to_reject": "The paper does not provide sufficient details about the experimental results. In particular, it is hard to assess whether the proposed method actually improves over previous methods.\n\n- The paper states that the method outperforms alternatives according to hypothesis tests, with \"p<0.5\". It is not clear what was tested, or how the data was analyzed. In addition, 0.5 is a very high type I error rate, which would typically be interpreted as consistent with noise.\n\n- The paper does not provide standard deviations or confidence intervals for any of its measurements.\n\n- The ablation results look marginal, and are difficult to interpret without more statistical analysis.\n\n- It is confusing how GPT-3.5 performed poorly on the tasks (much worse than existing baselines apparently), but was able to provide a useful supervision signal for outperforming the baselines. This seems like it should be unlikely. The paper should provide more explanation for what could be going on.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper investigates continual relation extraction, i.e. learning to extract relations in a continual learning setting. It proposes a method for distilling knowledge from LLMs to perform this task. The method uses reasoning produced by the LLMs for training smaller models. The paper finds that the proposed method outperforms previous methods on two CRE benchmarks. In addition, the paper reports some ablation analyses on the model.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "wgDxLFSz9d", "reviewer": "Reviewer_HVe5", "summary": "", "questions": "1. Line 078-083: The authors are requested to clarify this paragraph which requires more context around memory.\n2. Line 113-114: The authors are requested to provide more context around memory so that the reader does not have to refer to previous work. A suggestion: Please provide brief overview of Han et al., 2020 in the appendix.\n3. Line168-180: Please specify how many (top-k) analogous relations per relation are considered. For example, in Figure1, 2 analogous relations (“follows” and “characters”) are considered for relation “followed by”.\n4. Please include some future directions and any follow-up efforts in the conclusion section.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your insightful comments! Please see our reponses below.\n## Q1. The ablation study is not complete.\nThank you for your advice. We have done the additional ablation study and report the results in the following table. \n\n |  Models      | TACRED | FewRel |\n| ----------- | ----------- | ----------- |\n| RationaleCL | 80.8 | 85.1 |\n| w/o Taskd-cr | 80.3 | 84.9 |\n| w/o Taskr-cr | 80.6 | 84.9 |\n| w/o Taskd-cr & Taskr-cr | 80.2 | 84.8 |\n\nConclusively, the ablation study underscores the importance of both Taskd-cr and Taskr-cr components in enhancing the performance of the RationaleCL model. Their presence contributes to the model's robustness to analogous relations, indicating their roles in tackling the catastrophic forgetting challenges of continual learning effectively.\n\n## Q2. Clarification of memory-related statements in the article.\nWe will provide more details around memory in Line 078-083 and Line 113-114, which will be included in our final version. \n\nTo alleviate catastrophic forgetting in CL, previous rehearsal-based methods adopt an episodic memory module to store a few representative instances for each previous label. The memory data will be continually learned with emerging new tasks. When a new task is learned, we replay the memory contents associated with prior tasks to prevent forgetting. To select these representative instances for each relation, we use the K-means algorithm to conduct clustering based on the features of all samples extracted by T5-encoder, and select the samples in the centre of each cluster into memory. This approach can effectively preserve the essential label characteristics to address catastrophic forgetting.\n\n## Q3. How many top-k analogous relations per relation are considered?\nWe use cosine similary to judge analogous relations and set the threshold to be 0.97. We consider two relations\nare similar if their similarity exceeds 0.97.\n\nFor TACRED dataset, the total relation number is 40. The maximum number of analogous relations per relation is 5. The minimum is 2 and the average is 2.9.\n\nFor FewRel dataset, the total relation number is 80. The maximum number of analogous relations per relation is 4. The minimum is 1 and the average is 2.2.\n\n## Q4. Please include some future directions and any follow-up efforts in the conclusion section.\nFirstly, our method uses manually designed prompts to generate rationales, which may have a large impact on the quality of rationales. Investigating the impact of different prompts on the quality of rationales is an important direction for future research.\n\nSecondly, we currently only use generated rationale to avoid confusion among similar relations in continual learning. In the future, we can use the rationale generated by the LLMfor more complex tasks such as code completion, taks planning, etc.\n\n\nWe hope our answers can resolve your doubts. Please reconsider the review based on our answers.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.\tThe paper proposes the strategy “RationaleCL” which outperforms the strong baselines.\n2.\tThe paper presents ablation analysis and a case study (Appendix C).", "reasons_to_reject": "Incomplete ablation analysis in Table2. According to Line177-178 the purpose of cont. replay is to produce contrastive rationale (cri) and replace rationale ri in memory. Now, ri is involved in 2 tasks: Taskr and Taskd . When ri is replaced with cri in both the tasks, there should be 2 “new” tasks for cri. Let’s call them Taskr-cri and Taskd-cri\n\nIn Table 2, ablation study is conducted only for following strategies: w/o con. replay and w/o Taskd . However, a complete ablation analysis should include Taskr which is based on ri and one of the missing Tasks based on cri . Moreover, currently, simply mentioning “con. replay” in Table2 creates confusion about which task based on cri is being referred to.", "questions_to_authors": "1. Line 078-083: The authors are requested to clarify this paragraph which requires more context around memory.\n2. Line 113-114: The authors are requested to provide more context around memory so that the reader does not have to refer to previous work. A suggestion: Please provide brief overview of Han et al., 2020 in the appendix.\n3. Line168-180: Please specify how many (top-k) analogous relations per relation are considered. For example, in Figure1, 2 analogous relations (“follows” and “characters”) are considered for relation “followed by”.\n4. Please include some future directions and any follow-up efforts in the conclusion section.", "paper_topic_and_main_contributions": "For continual relation extraction, this paper advocates for the strategy “RationaleCL” which addresses the gap in the models’ capability to infer new relations which are analogous to the already seen relations. Specifically, RationaleCL is a question-answering based supervised technique having three subtasks: question to answer, question to rationale-answer and question-rationale to answer. Moreover, rationale is replaced with “contrastive rationale” to get more context while training the model. Experiments on 2 datatsets: FewRel and TACRED, demonstrate the effectiveness of the strategy.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "1.\tLine 043-044: Please correct grammar for this sentence: “Thus how to make a model learn current relations robustly to avoid sub-sequent confusion becomes the new research focus.”\n2.\tLine108-119: Please highlight Stage1 and Stage2 in Figure2. Currently, I am unable to confirm whether Figure2a and Figure2b are Stage1 and Stage2, respectively. Or Stage1 and Stage2 are within Figure2a. Text in Line108-119 should match the Figure2."}}, {"review_id": "uThrfTN8on", "reviewer": "Reviewer_ergu", "summary": "", "questions": "Question A: How is the loss for $Task_r$ (question to rationale-answer) calculated? It would be good to explicitly write the functions for $L_c$, $L_r$, and $L_d$ for clarity. \n\nQuestion B: In Figure 3, how similar are the relations with respect to the metric they were calculated (i.e., cosine similarity)? Is it possible to plot a list of all relations with their similarity and f1 scores to observe trends in model performance as relation similar increases/decreases?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your feedback and helpful suggestions on our work. We have carefully answered the questions in your comments.\n## Q1: How is the loss for $Task_r$ calculated?\nThe loss for $Task_r$ is calculated as $L_r = -log P(Y_k|concat(X_k, R_k))$, which represents negative log likelihood of the probability of generating the correct relation given the input text and rationale.\n## Q2: How is the similarity between relations calculated? \nAs mentioned in Line 168-171, we employ the cosine distance of the average embedding of the instances as a metric to measure the similarity of two relations[1].  \n## Q3: Improvements over other models and baselines are low.  Is there a stronger argument for the effectiveness of the method?\nTo further verify the effectiveness of our method, we analyse the correlation between F1 score improvements (compared with default setting w/o both) per relation and its similarity with other relations. We split all relations into 10 groups according to its maximum similarity with other relations. The similarity ranges from 0.90 to 0.99 for ten groups, each group is spaced 0.1. We calculate the average of the F1 increases for all relations in each group as the increase for the whole group.\n\n**FewRel**\n\n|  Similarity      | 0.90 | 0.91 | 0.92 | 0.93 | 0.94 | 0.95 | 0.96 | 0.97 | 0.98 | 0.99 | \n| ----------- | ----------- | ----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |\n| F1 improvements    | -0.45       | 0.37 |  0.41 | 0.23| 0.22 | 0.59 | 0.86 | 1.63 | 1.72 | 2.38 |\n\nThen we calculate the pearson correlation coefficient between similarity and F1 improvements. The result is 0.944, which indicates their strong correlation。\n\n**TACRED**\n\n|  Similarity      | 0.90 | 0.91 | 0.92 | 0.93 | 0.94 | 0.95 | 0.96 | 0.97 | 0.98 | 0.99 | \n| ----------- | ----------- | ----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |----------- |\n| F1 improvements    | -0.28       | 0.42 |  0.29 | 0.79| 0.62 | 0.83 | 1.23 | 1.45 | 1.48 | 1.62 |\n\nThe pearson correlation is 0.961.\n\nBased on our analysis above, it is evident that as the similarity between relations increases, our method yields more substantial improvements in F1 accuracy.\nWe will add a detailed tabular analysis of the correlations between cosine similarity and F1 score improvements in the final version.\n## Q4: Is there a post-processing step to filter invalid rationales?\nWe apologize for not including this due to space limitation. When generating the rationale, we use the following method to filter. Regarding the generated rationale, we also employ the LLM in reverse to classify the relation based on the rationale. In cases where we get incorrect results, we conduct a re-generation of the rationale through the LLM.\n\nWe hope our answer can resolve your doubts. Please kindly reconsider the review based on our answer.\n\n[1] Learning robust representations for continual relation extraction via adversarial class augmentation. EMNLP 2022.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) The paper demonstrates how rationale-tuning can be used for continual relation learning. In addition, given generation capabilities of LLMs, studying the effectiveness of using LLMs to augment data for such tasks is valuable.\n\n2) The paper is clearly written and the methodology is explained well.", "reasons_to_reject": "1) Improvements over other models and baselines are low. While ablation studies and analysis on specific examples of relations (e.g., “sibling”, “spouse”) as mentioned, a stronger argument for the effectiveness of the method could be made with additional analysis. For example, in what specific cases is the method most effective? Are there further trends that can be observed among these cases?\n\n2) There is no analysis regarding the quality of generated rationales. Was there a post-processing step to filter invalid rationales? This information would be good to include in the methodology for paper soundness.", "questions_to_authors": "Question A: How is the loss for $Task_r$ (question to rationale-answer) calculated? It would be good to explicitly write the functions for $L_c$, $L_r$, and $L_d$ for clarity. \n\nQuestion B: In Figure 3, how similar are the relations with respect to the metric they were calculated (i.e., cosine similarity)? Is it possible to plot a list of all relations with their similarity and f1 scores to observe trends in model performance as relation similar increases/decreases?", "paper_topic_and_main_contributions": "This paper introduces an approach for continual relation learning in language models. In particular, the method uses a LLM to generate rationales in order to learn relations. Their method involves two stages. The first stage involves training a language model on rationale-enhanced samples (generated by another LLM, which is GPT-3.5 in this paper) to facilitate the learning of relations. The second stage involves further training the language model on contrastive examples (also generated by GPT-3.5 in this paper) from memory between similar relations. \n\nEmpirical results show improvements over baselines on two relation extraction datasets: FewRel and TACRED. Additional analysis show improvements on specific cases of similar relations (e.g., “spouse”, “sibling”) and ablation studies show degradation with the removal of one or both stages of the method.\n", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "1) In Figure 2a, it would be helpful to color code the inputs and outputs for clarity. For instance, color the boxes $x_k$ and $y_k$ green, and the second $x_k$ and $r_k + y_k$ blue etc …\n\n2) When referring to the rationale-generating LLM, it would help to name it something more specific as opposed to “LLM”, which is vague and can cause confusion. For instance, “RGM” for “ rationale-generating model”."}}]

Author rebuttal:

Meta-review: This paper tackles the problem of continual relation extraction, particularly the problem of inferring new relations which have not appeared so far, but are analogous to the already seen relations. There approach is based on treating this as a question-answering problem and using LLMs for this task. The problem is interesting. All of the reviewers raised some concerns about the evaluation and raised the issue that the approach does not show great improvements over the considered baselines. However, I think the reviewers addressed this somewhat in the author response. I would urge the authors to focus on this aspect in the next version.
Decision: Accept-Main