ALCUNA: Large Language Models Meet New Knowledge
Authors: Xunjian Yin
Baizhou Huang
Xiaojun Wan
Venue: EMNLP 2023 Main (2023)
Keywords: Large Language Model
Model Evaluation
Knowledge
TL;DR: We construct a novel benchmark to assess the ability of large language models in face of new knowledge.

Abstract: With the rapid development of NLP, large-scale language models (LLMs) excel in various tasks across multiple domains now. However, existing benchmarks may not adequately measure these models' capabilities, especially when faced with new knowledge.
In this paper, we address the lack of benchmarks to evaluate LLMs' ability to handle new knowledge, an important and challenging aspect in the rapidly evolving world. We propose an approach called KnowGen that generates new knowledge by altering existing entity attributes and relationships, resulting in artificial entities that are distinct from real-world entities. With KnowGen, we introduce a benchmark named ALCUNA to assess LLMs' abilities in knowledge understanding, differentiation, and association. 
We benchmark several LLMs, reveals that their performance in face of new knowledge is not satisfactory, particularly in reasoning between new and internal knowledge. We also explore the impact of entity similarity on the model's understanding of entity knowledge and the influence of contextual entities. 
We appeal to the need for caution when using LLMs in new scenarios or with new knowledge, and hope that our benchmarks can help drive the development of LLMs in face of new knowledge.

ALCUNA: Large Language Models Meet New Knowledge
Xunjian Yin and Baizhou Huang" and Xiaojun Wan
Wangxuan Institute of Computer Technology, Peking University Center for Data Science, Peking University The MOE Key Laboratory of Computational Linguisties, Peking University {xjyin, hbz19 9, wanxiaojun]{pki edu. cn
Abstract
school admission tests, math competitions and so on (Hendrycks al., 2021; Guo et al., 2023; Zhong et al., 2023). LLMs also achieve promising results on these benchmarks.
With rapid development of NLP, large-scale language models (LLMs) excel in various tasks across multiple domains now. However, exist- ing benchmarks may not adequately measure these models' capabilities, especially when faced with new knowledge. In this paper, we ad- dress the lack of benchmarks to evaluate LLMs' ability to handle new knowledge, an important and challenging aspect in the rapidly evolving world. We propose approach called Know- Gen that generates new knowledge by altering existing entity attributes and relationships. re- sulting in artificial entities that distinct from real-world entities. With KnowGen, we intro- duce benchmark named ALCUNA assess LLMs' abilities in knowledge understanding, differentiation, and association. several LLMss, reveals that their performance in face of new knowledge is not satisfactory, particularly reasoning between new and in- ternal knowledge. We also explore the impact of entity similarity on the model's 's understand- ing of entity knowledge and the influence of contextual entities. We appeal to the need for caution when using LLMs in new scenarios or with new knowledge, and hope that our bench- marks can help drive the development of LLMs in face new knowledge.
However, it is surprising that there is not yet a benchmark to evaluate the ability of large models in face of new knowledge, which is very important and challenging. Why is this evaluation important? Firstly, we are in changing world, where models encounter new knowledge frequently in practice. And some work (Peng et al., 2023) is exploring retrieval methods to augment large models, which will also cause the models to meet new knowledge frequently. So of course we expect the model to perform well in such situations, because re-training the model every time is very expensive and unre- alistic. Secondly, as Elangovan et al. (2021) men- tioned, the presence of overlap between the train- ing and data can lead to misestimation of the model's memory ability as generalization ability, especially nowadays when LLMs are trained on an enormous amount data. Whereas, evaluation on new knowledge does not need to worry about such data leakage, as new knowledge can usually lead to new data and thus more reliable and valuable assessment of the model's ability.
benchmark
While such evaluation is important, it is chal- lenging to construct the benchmark. The reason is that it is difficult to ensure that the knowledge contained in the benchmark is new for LLMs, since training data for some models are large and non- publicly available. Furthermore, is also difficult to ensure that the knowledge used for benchmark- ing will be not outdated and inefficient, as there are many LLMs that may soon include data from the benchmark in their training. In summary, such benchmark for new knowledge needs to exhibit three basic characteristics: contains enough new knowledge for sufficient evaluation (sufficient), the knowledge is new to all models (model-agnostic) and the knowledge can remain new for a long time
1 Introduction
Large-scale language models (LLMs) have made impressive progress in the last few years (Brown et al., 2020; Ouyang et al., 2022; Touvron et al., 2023; OpenAI, 2023), which perform surprisingly well various tasks various domains, to the extent that many traditional benchmarks (Thorne et al., 2018; Wang et al., 2018) are no longer suffi- cient to measure the capabilities of LLMs. There- fore, some new benchmarks have been proposed to evaluate the ability of the model to solve more complex tasks such as college entrance exams, law "These authors contributed equally to this work.

---

(long-lasting).
1) we propose new method KnowGen to generate new knowledge for simulating real scenarios. 2) we apply our method to produce an artificial bio- logical entity knowledge dataset, ALCUNA, as a benchmark for evaluating the performance of mod- els in face of new knowledge. 3) we evaluate and analyze several popular large models and obtain insightful observations and conclusions.
There are several possible solutions to the above challenges. One option is to always use the most updated data such as the news of the day (temporal knowledge). However, this is both labor-intensive race against the LLM training and unclear about the lifetime of the proposed data. Another option is keep the benchmark closed-source, with an au- thoritative committee managing the data and users calling API when evaluating, thus preventing using them for training LLMs. To reach this point further requires community coordination.
Our benchmark has been released to the commu- nity to facilitate future research
Algorithm 1: Knowledge Generation
To better address these challenges, we propose an approach to GENerate new KNOWledge conve- niently KnowGen) based on the structured repre- sentation of existing entity knowledge by making reasonable changes to entity attributes and rela- tionships. There are differences and associations between artificial entities and existing entities. Par- ticularly, we apply KnowGen with structured bi- ological taxonomic information data rationally create a group of organisms that do not exist in the world. To test the model's ability face of new knowledge, we construct a variety of ques- tions about these artificial entities that can examine the model's 's ability to understand new knowledge (Knowledge Understanding), distinguish between model's internal knowledge and new knowledge (Knowledge Differentiation) and make multi-hop reasoning by linking model's internal and new knowledge (Knowledge Association). We use the Artificially ConstrUcted kNowledge to Assess LLMs as a benchmark CUNA).
input :One Class C
output:Properyy Set ((ē) é
e" + RandomSelect(())
E <- sib(e")
I/ Get the triplet set for heredity, variation, dropout
and extension
Th Th Th RandomSplit(TR((ee
Tx Tx. Tx <- RandomSplit
T + RandomSample(T
Heredity and Dropout
Ta(e) + U
Ta(e) < TaCC
Variation: replacing the object with an entity from the class
for (e", e') in T do
Edab + sib(e')
ea <- (and
TR(a) + Tx(e) ((ē, r, e²
Variation: add gaussian noise to the value or copy
from grub
for (c", a, v) in Tx do if (o) then $ t- u N(0, else
We evaluate and analyze several popular large models based on ALCUNA, including !hatGPT! Alpaca, Vicuna, and ChatGLM with vanilla, CoT (hhoihoo-Thhought), Shrot and Few-Shot set- tings (Brown et al., 2020; Kojima et al., 2023; Wei et al., 2023). We find that neither ChatGPT nor other models perform very well in face of new knowledge. ChatGPT does a good job of under- standing and differentiating new knowledge, but almost all models fail to reason between the new knowledge and the internal knowledge. This re- minds us to remain cautious when large models encounter new scenarios and knowledge. In addi- tion, we explore the impact of entity similarity on the model's 's understanding of entity knowledge, the impact of contextual entities, etc.
204 <- (andomSelect
i + GetValue a)
(x + (x ((,, a, ()}
Extension and get final property
T(e) + Un(é)
2 KnowGen: Knowledge Generation
In this section, we begin by presenting our inspira- tion, then formally introduce our knowledge gener- ation method KnowGen.
2.1 Inspiration
According to the ontological form (Sowa, 1995; Noy et al., 2001), represent most knowledge
The contributions of our work are listed below:
Phttps://chat... wwwppaap......aa.......appppaat. com/chat
Phttps: / thub. www/Arriidpku/ALLOOO

---

entities, the classes which they belong, their attributes and the relations between them. And in- spired by organisms: organisms can produce organ- isms with new properties naturally through heredity and variation. Can knowledge also be "inherited" and "varied" in broader context? Different enti- ties of the same class have different properties as well as commonalities. Generally speaking, enti- ties of the same class similar some extent, while they have some different properties. By anal- ogy with biological terminology, we refer to this characteristic of knowledge of similar entities as "hybridizabilitt". This inspires us to use different entities of the same class to fuse their properties, simulating the process of biological inheritance and variation, to generate new entity knowledge.
within a specific class, it must adhere to certain common properties shared by other entities in the same class. Furthermore, ficial entity to possess some unique properties that differentiate it from existing entities. To address these requirements for individuality and common- ality, we propose fast and effective method to construct an artificial entity that fuses attributes and relations of entities within the same class.
essential for the arti-
Initially, we select an existing entity e" from a specific class C' to serve as the parent entity for our artificial entity ė and consider other entities within the same class C as sibling entities of par- ent, denoted by sib(e") = ] Our goal is to construct an artificial entity that exhibits high similarity to the parent entity and conforms to the commonality of the class (heredity) while incorpo- rating properties from the sibling entities or reason- ably changing property values (variation). As an example, in Figure I, the artificial entity Alcuna inherits the "Diet" and other attributes from the parent Alpaca, while the "Body mass" is varied.
In the following will formally define and describe how knowledge is "inherited" and "varied" in our approach.
2.2 Knowledge Formalization
Based on the design of the ontology. We repre- sent knowledge from the viewpoint of Entity, En- tity e E E is a distinct and identifiable object or individual in the world. Each entity can possess various attributes E A. which describe the prop- erties of the entity with a value v E V. At the same time, each entity can participate in relations r R with other entities. Both the attributes and relations of entity e can be represented as a set of property triplets: {(c, a; v)} = Tx(e) c TA and {(e, r, r'))} = Tn(e) C TR- Entities with similar characteristics may be aggregated into a class C. C E. For convenience, we denote the same properties across all entities in class C' as T(C) Ta(C) U TR(C). Without any special description, the triplet (e) of entity e refers to its unique properties.
Besides the above operations of heredity and variation, construct new entities with additional extension and dropout of the properties of the new entities, in order mimic human progressive cog- nitive processes of entities. As the example in Figure I shows, we extend the attribute of "First appearance" from Vicuna to Alcuna, and drop out the "Life span" from the parent entity Alpaca.
The whole method can be seen in Algorithm 1. A detailed description of KnowGen with natural language and expressions is shown in Appendix A. The entities constructed in this way are not only reasonable but also convenient for us to assess the model's cognitive ability to associate and differen- tiate new knowledge with the existing knowledge.
2.4 Question Answering as Evaluation Task
For example, Figure 1 shows an example in the form of such structured knowledge, where both Alpaca and Vicuna are existing entities belonging to the class Camels and Alcuna is an artificial entity generated by us. Alpaca has attributes such as "Body mass" and relations such as "Eaten by", and can be formed into triplets such as (Alpaca, Body mass, 60kg) and (Alpaca, Eaten by, Cougar).
Based on the constructed artificial entities, natural form of evaluation is ask questions to LLMs in the context of new knowledge. In specific, we lever- age an attribute triplct (ě, a, v) for generating a one- hop question q(ē, a, v) by specifically asking about the object v, given the subject é and attribute a. With chain of relation triplets To = (ě,r,e1) -> (c1, Fl, .2)
-> TEN-1,TN-1,C2 we con- struct multi-hop question (TTc) asking about the tail object eN. given the head subject and rela- tions {r. "r,r,,,.,,NNI,,
2.3 Construction of New Entities
Focusing on knowledge in the form of ontology, we aim to construct artificial entities reasonably to ac- celerate the process of new knowledge generation in the real world. When creating an artificial entity
We propose that LLM requires the knowl- edge understanding, knowledge differentiation and

---

Knowledge Understanding (KU):
Compete
What type of foed is incloded in the diet of Alcuna? Answer: leaves In which geological period did Aleuna first appear? Answer: middle Pleistacene age
Cougar
Jaguar
Maned Wolf
Eaten by
Eaten by
Camels
Rody mass: 60kg
Body mass: 47.5kg
Knowledge Differentiation (KD);
Life span: 25. 25.8ysurs
First uppearance: middle Pleistocene age
Does Alcuna have hody mass of 60kg?
A paca
Diet: leaves
Properiie....
Vicuna
Weaning age: months
Answer: No.
What's the average life span of Alcuna?
Answer: I don't know.
Heredity
Varlation
Existing
Artificial
Knowledge Association (KA):
Body mass: 56.5kg [variation]
What organism is the competitor of the Alcuna's is natural enemy??
Diet: leaves [heredity]
Life Life-Span [dropout]
Wolf.
First appearance: middle Pleistocenc age [extension] Other Properties inherited from Alpaca...
Alcuna
Figure I: Demonstration of ALCUNA, including heredity, variation, extension and dropout operations in KnowGen, generated artificial entity named Alcuna and three types of questions related to it.
knowledge association abilities when faced with new knowledge. To enable more detailed assess- ment, design diverse categories of questions to evaluate each ability, as shown on the right side of Figure 1.
triplets, we utilize them to evaluate the ability of remembering and understanding new knowledge by simply asking questions about the objects in triplets. We group such questions into KU question set.
Specifically, we sample some attribute triplets in the variation set (ē) and the dropout set (E) to create KD question set, which is ideally suited for evaluating the knowledge differentiation abil- ity of model to distinguish between the parent entity e existing in its internal knowledge and the newly constructed artificial entity è provided in the context.
ALCUNA: Our Benchmark
With our proposed method, can create large amount of new entities quickly based exist- ing structured knowledge. A natural attempt is to construct new organisms on already discovered ones, since the biological taxonomy is perfectly adapted to our proposed approach. Therefore, we utilize KnowGen to propose A1.CUNA, biological dataset for evaluating the ability of the model in face of new knowledge shown in Figure 1.
The proficiency of LLMs in reasoning along mastered knowledge graph has been demonstrated in previous studies (Hu et al., 2022; Choudhary and Reddy, 2023). However, it remains uncertain whether can effectively establish connection be- tween newly encountered artificial entity and the existing knowledge for multi-hop reasoning task. To investigate this, incorporate the relations of artificial entity construct a new graph, which en- compasses both existing entities and the artificial entity. We then perform a breadth-firs--eet scrrt- on the relation graph to identify a chain of relation triplets To with the artificial entity serving as root (e.g., [(Alcuna, Eaten by, Jaguar), (Jaguar, Com- pete with, Maned Wolf))), and then utilize the chain generate multi hop question Q(Tc) (e.g., What organism is the competitor of the Alcuna's 's natu- ral enemy?). We group such questions into KA question set.
3.1 EOL Database
We utilize the structured data from the EOL (Ency- clopedia of Life) database (Parr al., 2014) to pro- vide existing knowledge, which is an online, freely accessible database that aims to provide informa- tion about all known species on Earth. EOL orga- nizes all biological taxons into taxonomic tree, in which each entity belongs to class. The most intriguing feature of EOL is that it constructs rich structured data in the form of key-value pairs for each biological entity, including taxonomic rank, attributes, relationships and information source. As a whole, it contains 2404790 entities with a total of 13625612 properties consisted 669 property types. The substantial volume of data, coupled with
For the rest of the artificial entity's property
Phttps://eol.or//

---

its well-organized format, renders BOL the most suitable data source for constructing ALCUNA.
3.2 Artificial Entity Construction Details
4finiics
244
1.3
Each time select class C from the taxonomic tree of EOL and consider its members as entities. We then divide them into one parent entity el and its siblings sib(e") from which we construct the artificial entity. Since a high-level taxon is usually not a specific organism, the properties it possesses may be too homogeneous, so we screen out all taxons belonging to kingdom, phylum and domain. In the real world, the naming scheme of newly found creature usually incorporates the same prefix or suffix other creatures the same species. In order to mimic the real world scenario and consid- ering the tokenization algorithms used in LLMs, we firstly split the names of related existing enti- ties (i.e. the parent entity and sibling entities of parent) into subwords Then randomly select names of related entities, and for the i-th selected entity we choose its i-th subword. For example, "ALCUNA" created from Alpaca and Vicuna.
0.5
15
20 25
4AAtribuls
PRelatiora
Figure 2: (a) The number of entities with different counts of attributes. (b) The number of entities with different counts of relations.
3.4 Dataset Summary
With the previous steps, we constructed dataset, ALCUNA, for evaluating the ability of LLMs face of new knowledge. The ALCUNA dataset consists of total of 84351 questions about 3554 artificial entities. We ensure that the constructed artificial entities contain rich and unique attributes and relationships by filtering out parent entities with less than three properties. Specifically, each artificial entity contains 11.75 property triples and 25.39 siblings on average. The distribution of the number of property triplets is shown in Figure 2. We organize the dataset in terms of questions, and for each question we collect the correspond- ing property triplets as evidence and the relevant artificial entities' information as new knowledge. We divide all questions into three subsets, KU, KD and KA, as mentioned in Section 2.4 mea- sure the corresponding capabilities of LLMMs in a fine-grained manner. specific, KU, KD and KA contain I 1136,, 27 186 and 15353 questions respec- tively. The details about question forms in the three subsets are shown in Appendix B.
3.3 Template-based Question Generation
Given a property triplet (ē, a, v) (one-hop setting) or chain of property triplets To = (,) (ei,ri,eh 22) + + (EN-1, TN-1, ex) (multi-hop setting), we aim to generate natural language ques- tion asking about the tail object.
We leverage ChatGPT in the process of question generation to avoid expensive labor costs following Petroni al. (2019). Specifically, use ChatGPT to generate question template with placeholder [T] given only the relevant properties to avoid intro- ducing too much model's knowledge of a specific entity. Then we generate questions from the ques- tion template by replacing [T] with the name of head subject. We generate five question templates for each property group in form of multiple choice, fill-in-the-llank and Boolean questions. The details about the prompts used for question generation and examples are shown in Appendix B.2. To ensure the quality of automatic question generation by this method, we randomly sample 100 questions each for one-hop and multi-hop questions for human checking. It turns out that for the generated one- hop questions, 98% correct; for the multi-hop hop questions, 95% correct. It shows that this way of constructing questions is acceptable.
4 Evaluation of LLMs
4.1 LLMs Selected for Evaluation
We select several popular LLMs for evaluation and analysis on our benchmarks, including ChatGPT, ABpaca-7B, Vicuna-13B and ChatGLM-6B. The detailed description of our selected models can be found in Appendix C.
4.2 Evaluation Methods
In order to adapt the approach in the of large models and match the application scenarios in practice we introduce two types of evaluation methods: zero-shot and few-shot. We implement both the vamilla and "Chain-of of-Thought" (CoT) rea- soning forms for zero-shot and few-shot setting.
We utilize the tokenizer of GPT-2 for tokenization.

---

For experiments in the zero-shot setting, our inputs are structured representations of new knowledge and questions to be answered by the model. For experiments in the few-shot setting, we include sev- eral examples of the same form together with the answers, which we hope will help the model un- derstand. For the zero-shot CoT, we append "Let's think step by step." at the end of questions, and for the few-shot CoT,, the reasoning process for the answers of examples is also attached. Please refer to Appendix D for the detailed method description. An example of prompt used in our experiment is shown in Table 12.
5
Result and Analysis
5.1 Overall Results
The performance of the LLMs on our benchmark under different settings is shown in Table 1. We can see that ChatGPT has the best performance in all settings, which is consistent with our usual beliefs. Vieuna has the second best performance among all models. In terms of methods, the few-shot setting performs better than the zero-shot setting overall, and CoT performs better than the vanilla form in most cases.
In face of new knowledge, as seen in Table 1, LLMs do perform poorly except for ChatGPT on KU and KD experiments. Among all abilities, knowledge association is obviously the most diffi- cult for LLMs, and all of them have difficulty in relating to their internal knowledge through new knowledge provided, and thus making multi-hop reasoning correctly. The performance of knowl- edge understanding and knowledge differentiation is better than that of knowledge association, but yet not satisfactory for most LLMs.
4.3 Evaluation Metric
Since our questions are in the form of multiple choice, fill-in-the-blank or Boolean questions, the golden answers are usually just one few words. Therefore, we determine the correctness of the model output by matching it with the answer (like Accuracy). Since there may be more than one possi- ble answer to some questions, such as those asking about the geographical distribution of entities, etc., we consider the answer to be correct as long as it matches one of the correct answers. This is less stringent measurement, but as seen in Section 5, most models still perform poorly. Using the proposed dataset, we evaluate each model's ability with each method for knowledge understanding, knowledge differentiation, and knowledge associa- tion, respectively. We report the average score on the entire benchmark in each setting.
In summary, current LLMs perform relatively poorly in face of new knowledge, slightly better in knowledge understanding and knowledge dif- ferentiation, and have more difficulty in reasoning across new and existing knowledge. In order to have a clearer view of models output, please refer to Appendix F for the analysis of models output. Considering that it is expensive, slow and un- stable call ChatGPT's API, without loss of gen- erality, all the following comparison experiments for analysis are conducted on three other models. In addition, for convenience, the following analy- sis experiments are performed in the setting of the vanilla few-shot method, and structured input arti- ficial entity knowledge, if not specifically stated.
4.4
Data Filtering
Since there are differences of internal/existing knowledge of different models due to the differ- ent model size and training data, we further fil- ter the samples in our dataset for each model be- fore testing, based on previous work (Petroni et al., 2019), in order not to be influenced by the differ- ence (which is not the theme of our paper) and to compare the models' performance in face of new knowledge in more focused and fair way. For our method of filtering questions, please refer to Appendix E. We experiment and analyze the four models mentioned in Section based on the fil- tered new knowledge, using the evaluation settings introduced in Section 4.2.
5.2 Impact of Entity Similarity
In this section we explore the effect of the similar- ity between the artificial entity and the parent entity on the model performance over the KD questions, which are designed assess the model's ability to distinguish between new knowledge and exist- ing knowledge. Specifically, we explore attribute similarity and name similarity.
The More Similar, the More Confusing (unless powerful enough) We define the proportion of overlap of properties between entities as the prop- erty similarity of entities. As shown in Figure 3,

---

ChaiGPT
Alpaca
Vicuna
ChaIGLM
ChatGPT
Alpaca
Vicuna
ChaIGLM
Bero-Shot aaan-lltt-Vanilla
Zero-Shot-CoT
KU
50.19
31.02
34.12
34,64
68.75
39.81
39.61
24.85
KD
58.70
15.35
38.65
32.29
61.78
14.29
38.53
22.84
KA
28.44
24.60
29.71
10.29
35,36
19.66
29.55
4.97
Avg.
52.85
25.12
35.98
31.26
63.34
29.44
38.00
22.04
Few-Shot- Vanilla
Few-Shot-CoTT
KU
75.44
33.80
41.22
47.97
82.18
40.77
43.67
40.91
64.20
38.97
46.76
41.42
74.99
36.24
55.81
37.19
41.52
27.63
30.10
27.47
37.88
25.73
25.07
26.93
Avg.
64.37
35.09
42.74
42.56
74.11
38.02
47.73
37.64
Table I: Performance of LLMs at our benchmark under different settings
0.7
Context
Alpaca
Vicuna
CharGI.M
artificial
38.97
46.76
41.42
w/ parent
37.09
35.04
24.71
Avg Score
0.5
w/ irrelevant
37.12
37.17
35.00
0.4
Table 3: Results on KD questions with different knowl- edge in context.
0.2
Chiuda.de
assign two different names to artificial entities with the same properties: one a randomly generated sequence of characters (random), and the other a random substitution of one character (similar) for the name of its parent entity. The results of the experiments on the KD questions ble 2. We can find that the influence of names model cognition does exist, and similar names are more likely to cause confusion in the model. How- ever, the effect is not very large and both results are lower, which shows that the modeling of new entities by LLM is both derived from the names and influenced by the properties.
Siaaa 12g
0.0
0.2
0.3
0.4
0.5
0.9
Property Similarity
Figure 3: Relationship between model performance on KD questions and the property similarity between the artificial entity and its parental entity.
shown in Ta-
Alpaca
Vicuna
ChatGLM
similar
38.74
47.23
41.61
random
39.52
47.64
43.20
Table 2: Results on KD questions of the entity with the same property and different name.
divide questions according to the property sim- ilarity between the parental entities and the artifi- cial entities, and calculate the scores of models in different similarity ranges on the KD problem re- spectively. We can find that the performance of the robust ChatGPT to differentiate entities is almost independent of the similarity. But other than it, all other models are affected by entity similarity. The more similar the new entities to the existing entities, the harder they are differentiate them, which illustrates the flaws of LLMss. The further analysis shown Appendix ??
5.3 Impact of Provided Knowledge
To further explore the performance of LLMs when new knowledge is provided in the context, we vary the knowledge content provided for the analysis experiments.
Parent Entity Aggravates Confusion Accord- ing to Section 5.2, the models suffer from a certain degree of confusion when faced with new knowl- edge that overlaps in name or properties with inter- nal knowledge. To further verify this, we explicitly introduce parent entities in the context. Specifi- cally, conducted 1) adding parent entity knowledge to the context; 2) adding a random existing entity knowledge as control variables. As shown in Table 3, the per- formance is affected by both parent and irrelevant
The Name also Plays a Role Since each artificial entity is identified by its name, think that the name might have some effect on the model's abil- ity to distinguish new knowledge, so we conducted experiments on KD questions for comparison. We
comparison experiments:

---

Context
Alpaca
Vicuna
ChaIGLM
be seen from Table 5, all models generally perform better with the structured input setting (JSON). The models' understanding this structured text may come from the code in the training data. This indi- cates that for this kind of high-density knowledge imput, clear and structured representation is more helpful for the model's understanding.
artificial
27.63
30.1
27.47
w/ chain
29.21
33.25
44.43
w/ irrelevant
25.38
22.98
18.56
Table Results on KA questions with different knowl- edge in context.
Alpaca
Vicuna
ChatGLM
Assess New Models with ALCUNA
JSON
ISON
NL
JSON
NL
KU
33.8
30.63
41.22
28.04
47.97
20.07
In this section, we discuss how to apply the pro- posed ALCUNA benchmark to other models. There are two different application scenarios of our bench- mark. First, if one wants to assess the knowledge understanding performance of different LLMs in the face of new knowledge, ALCUNA can be di- rectly utilized for evaluation. On the other hand, if one wants to compare the knowledge differenti- ation and association abilitics, the different back- ground knowledge inside the different models may lead to an unfair comparison. Therefore, we need to conduct an additional filtering on AI.C to ensure the existing entities are possessed by all models, which will cause a shrinkage of our bench- mark. Despite this, the current benchmark has been filtered on models such as Alpaca. A reasonable assumption is that the models that come after that will be more and more powerful, so the resulting shrinkage won't be very severe.
KD
38.97
36,00
46.76
36.12
41.42
22.44
KA 27.63
26.07
30.10
25.48
27.47
9.82
Avg. 35.09
32.16
42.74
31.92
42.56
20.54
Table 5: Results Al.CunA with knowledge in JSON and natural language format (NL).
entities in the context, which is consistent with pre- vious work (Shi et al., 2023). More significantly, for Vicuna and ChatGLM models, the parent entity brings more substantial performance degradation compared to the irrelevant entity, again confirming the confusion problem of existing large models in face of new knowledge.
Chain Entities are Key to Knowledge Associa- tion To more clearly analyze why all models per- forms poorly the knowledge association prob- lem, conduct two additional sets of experiments on the KA questions: 1) adding knowledge about the entities involved the reasoning chain to the context. 2) randomly sampling the same number of entities to the context for a fair comparison. The final results are shown in Table 4. We can find that the score of all models improves very much after adding the information of the entities required for inference, and the performance of all models decreases after adding irrelevant entities. This also shows that the main problem that LLMs really cannot make the association between new and ex- isting knowledge well, and not just the problem of not being able to make reasoning.
Related Work
Large Language Models In recent years, sig- nificant advancements in Large Language Models (LLMs) like FLAN-IsSChung et al., 2022), GPT- 3(Brown et al., 2020), OPT(Zhang et al., 2022), LLama(Touvron et al., 2023) and GPT-4(OpenAT, 2023) have led to exceptional performance in nat- ural language processing tasks. At the same time, open-source LLMs based on LLama and other fun- damental models for further instruction fine-tuning have emerged recently, such as A Alpaca(TTaor et al., 2023), Vicuna(Chiang et al., 2023), Koala(Geng et al., 2023), ChatGLM(Du et al., 2022), etc., which have also shown strong capabilities.
Structured Knowledge is Better Since our knowledge is represented structurally. the input of knowledge in our experiments is also structured, shown in Table 12. To explore the effect of the addition- form of the knowledge representation, ally do comparison experiments with knowledge in natural language form as input (NL). We use tem- plates to transform each attribute into language description for input similar to the template- based question generation process in Section 3.3. As can
These models have shown breakthroughs variety of tasks, and some have been applied as a commercial product in daily work. Since the world constantly changing, the ability of the models perform when faced with new knowledge is critical.
Existing Benchmarks Benchmarks, such as SQuAD(Rajpurkar et al., 2016), SNLI(Bowman

---

al., 2015), GLug et al., 2018), Super- GLUE (Wang al., 2020), LAMBADA (Paperno et al., 2016), etc., is essential for setting evaluation criteria and tracking model performance. While some traditional benchmarks like SQuAD and SNLI assess single-task abilities, GLUE and Su- perGLUE evaluate general language models across various NLP tasks. More recently, with rapid de- velopment of LLMs, new benchmarks have been proposed evaluate on more complex tasks such as college entrance exams, law school admission tests and so on (Hendrycks et al., 2021; Guo et al., 2023; Zhong et al., 2023). However, these bench- marks are still based on existing knowledge, which is abundant in the training data of LLM.
Limitations
Although the method design can be used for the construction of any knowledge that satisfies the ontological representation, we have implemented only on biological data. It absolutely possible to use this approach to create new knowledge other domains for evaluating LLMs. It is because KnowGen method for creating new knowledge only requires some defined classes, and some entities in the classes, entities with their own attributes, and connections between the entities. Any knowledge base with such a structure can be used create new knowledge with our KnowGen method. In the future, we hope that new knowledge datasets from more domains will be available.
Some knowledge benchmarks (Onoe et al., 2021; Mallen et al., 2023; Arodi et al., 2023) evaluate model's ability of knowledge integration while they either only evaluate small models which are very different with LLMs (training data, ability, etc.) or simply use knowledge that already exists. There- fore, benchmarks that assess LLMs' ability with new knowledge rather than existing knowledge are urgently needed.
We evaluate only few powerful models due to the fact that some models closed source the number of parameters is too large. We expect that more models can be measured using our bench- mark.
Ethics Statement
The knowledge of biological entities in our bench- mark artificially constructed, so it does not exist in the real world. Therefore, for humans, it is neces- sary to be careful not to be misled by the knowledge inside; for models, the hazard of using our dataset for training on model knowledge is unknown. This is in line with our expectation that we want AL- CUNA be used new knowledge benchmark only for the evaluation of model capabilities in face of new knowledge.
Source of New Knowledge Many works use tem- poral knowledge source of new knowledge to evaluate new knowledge behavior of LLMs (Lazari- dou et al., 2021; Agarwal and Nenkova, 2022; Jang al., 2023; Zaporojets et al., 2023). There is also some work that uses entity or attribute substitution to create new knowledge (Longpre et al., 2022; Zhou et al., 2023), A discussion of the differences and strengths and weaknesses of our work versus prior work is in Appendix I.
Acknowledgements
This work was supported by National Key R&D Program of China (2021 YFF0901502), National Science Foundation of China (No. 62161 160339, State Key Laboratory of Media Convergence Pro- duction Technology and Systems and Key Lab- oratory of Science, Technology and Standard in Press Industry (Key Laboratory of Intelligent Press Media Technology). We appreciate the anonymous reviewers for their helpful comments. Xiaojun Wan the corresponding author.
8 Conclusion
We propose a new approach KnowGen construct new knowledge and build an artificial biological en- tity benchmark ALCUNA for evaluating the ability of LLMSs faced with new knowledge. We test and analyze several popular models with commonly used methods, and find some useful conclusions. Our proposed approach and benchmark can help the development of more powerful LLMs that can understand, differentiate and reason across new and existing knowledge. In the future, we expect that more L.LMs will be evaluated on our benchmark. More benchmarks in other disciplines also can be constructed based our method.
References
Oshin Agarwal and Ani Nenkova. 2022. Temporal ef- fects on pre-trained models for language processing tasks.

---

Akshatha Arodi, Martin Pömsl, Kaheer Suleman, Adam Trischler, Alexandra Olteanu, and Jackie Chi Kit Che- ung. 2023. The kitmus test: Evaluating knowledge integration from multiple sources in natural language understanding systems.
Biyang Guo, Xin Zhang, Ziyuan Wang, Mingi Jiang. Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023. How close is chatgpt to human experts? comparison corpus, evaluation, and detection.
Dan Hendrycks. Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021. Measuring massive multitask language under- standing.
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large anno- tated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empiri- cal Methods in Natural Language Processing, pages 632-642, Lisbon, Portugal. Association for Compu- tational Linguistics.
Ziniu Hu, Yichong Xu, Wenhao Yu, Shuohang Wang. Ziyi Yang, Chenguang Zhu, Kai-Wei Chang, and Yizhou Sun. 2022. Empowering language models with knowledge graph reasoning for question answer- ing.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanic Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler. Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners.
Joel Jang, Seonghyeon Ye. Changho Lee, Sohee Yang. Joongbo Shin, Janghoon Han, Gyeonghun Kim, and Minjoon Sco. 2023, Temporalwiki: A lifelong bench- mark for training and evaluating ever-evolving lan- guage models.
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yu- taka Matsuo, and Yusuke Iwasawa. 2023. Large lan- guage models are zero-shot reasoners.
Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gri- bovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d' Autume, Tomás Kociský, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, and Phil Blunsom. 2021. Mind the gap: Assessing temporal generalization in neural language models. In Neurul Information Processing Systems.
Wei-Lin Chiang. Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang. Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric Xing. 2023. Vicuna: An open- source chatbot impressing gpt-4 with 90%* chatgpt quality.
Nurendra Choudhary and Chandan K. Reddy, 2023. Complex logical reasoning over knowledge graphs using large language models.
Shayne Longpre, Kartik Perisetla, Anthony Chen, Nikhil Ramesh, Chris DuBois, and Sameer Singh. 2022. Entity-based knowledge confficts in question answering.
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddbartha Brahma, Al- bert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdh- ery. Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Ja- cob Devlin, Adam Roberts, Denny Zhou, Quoe V. Le, and Jason Wei. 2022. Scaling instruction- finetuned language models.
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023. When not trust language models: Investigating effectiveness of parametric and non-maramttric mem- ories.
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettle- moyer. 2022. Rethinking the role of demonstrations: What makes in in-context learning work?
Zhengxiao Du. Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022. Glm: General language model pretraining with autoregres- sive blank infilling. In Proceedings of the 60th An- nual Meeting of the Association for Computational Linguistics (Volume I: Long Pupers), pages 320-335.
Natalya F Noy. Deborah L McGuinness, et al. 2001. Ontology development 101: A guide to creating your first ontology.
Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi, and Greg Durrett. 2021. Creak: A dataset for common- sense reasoning over entity knowledge.
Aparna Elangovan, Jiayuan He, and Karin Verspoor 2021. Memorization vs. generalization: Quantifying data leakage in nlp performance evaluation.
OpenAL. 2023. Gpt-4 technical report.
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Car- roll I.. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray. John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder,
Xinyang Geng, Arnav Gudibande, Hao Liu, Eric Wal- lace, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023. Koala: A dialogue model for academic re- search. Blog post.

---

Paul Christiano, Jan Leike, and Ryan Lowe. 2022. Training language models to follow instructions with human feedback.
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023. Llama: Open and efficient foundation language models.
Denis Paperno, Germán Kruszewski, Angeliki Lazari- dou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 2016. The LAMBADA dataset: Word prediction requiring a broad discourse context. In Proceedings of the 54th Anmual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 1525-1534, Berlin, Germany, Association for Computational Linguistics.
Alex Wang. Yada Pruksachatkun, Nikita Nangia, Aman- preet Singh. Julian Michael. Felix Hill. Omer Levy, and Samuel R. Bowman. 2020. Superglue: A stickier benchmark for general- purpose language understand- ing systems.
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018. GLUE; A multi-task benchmark and analysis platform for nat- ural language understanding. In Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. pages 353-355, Brussels, Belgium. Association for Com- putational Linguistics.
Cynthia S Parr, Mr Nathan Wilson, Mr Patrick Leary, Katja Schulz, Ms Kristen Lans, Ms Lisa Wal- ley, Jennifer A Hammock, Mr Anthony Goddard, Mr Jeremy Rice, Mr Marie Studer, et al. 2014. The encyclopedia of life v2: providing global access to knowledge about life on carth. Biodiversity data journal, (2).
Baolin Peng. Michel Galley, Pengeheng He, Hao Cheng. Yujia Xie, Yu Hu, Qiuyuan Huang. Lars Liden, Zhou Yu, Weizhu Chen, and Jianfeng Gao. 2023. Check your facts and try again: Improving large language models with external knowledge and automated feed- back.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou, 2023. Chain-of-thought prompting elic- its reasoning in large language models.
Klim Zaporojets, Lucie-Aimee Kaffee, Johannes Deleu, Thomas Demeester, Chris Develder, and Isabelle Au- genstein. 2023. Tempel: Linking dynamically evolv- ing and newly emerging entities.
Fabio Petroni, Tim Rocktäschel, Patrick Lewis, An- ton Bakhtin, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2019. Language models as knowl- edge bases?
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher De- wan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mi- haylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022. Opt: Open pre- trained transformer language models.
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natu- ral Language Processing, pages 22932,39,, Austin, Texas. Association for Computational Linguistics.
Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. 2023. Agieval: A human-centric benchmark for evaluating foundation models.
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Fd Chi, Nathanael Schärli, and Denny Zhou. 2023. Large language models can be easily distracted by irrelevant context.
Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen. 2023. Context-faithful prompting for large language models.
John F. Sowa. 1995. Top-level ontological categories. International Journal of Human- Computer Studies, 6365).669-685.
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang. and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction- instruction-folloiingg llmaa model. https:// gi githu..ccom com/ wttp:///cttttuttttteeeee wattassllaabsseeee http//stafford_aaapg
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018.
FEVER: a large-scale dataset for fact extraction
and VERification. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 809-819, New Orleans, Louisiana. Association for Computational Linguistics.

---

A KnowGen Method
multiple choice
Boolean
fill-inttheblank
KU
2459
4232
4625
Here, we will describe knowgen in the form of natural language and math formulas.
KD 3487
34497
19698
KA 15353
Following the symbolic representation in Sec-
Total
21299
38729
24323
tion 2.2, divide the property triplets T(e)
(a(e) U (R(e) of parent entity into three sets: re- main, change and delete, denoted as (e)
Table 6: Number of different forms of KU, KD and KA questions.
(e(e) U (c), E dr..dd. We randomly sam- ple some triplets from sibling entities as add set (2) - ( U TR()). Subsequently, we fuse the above four sets" to form the properties of ar- tificial entity. We retain (e) for high similarity between the parent and artificial entities.
Total
ChatGPT
Vicuna
Alpaca
ChatGLM
Refuse
20.01
17.3
20.69
17.45
Multi
0.5
0.2
0.04
0.06
Wrong
79.49
82.5
79.27
82.49
Total
100.00
100.00
100.00
100.00
@ = [(2, a, e)((e, a) E Tx(e)) T = ((,,,, & Th(e))
Table 7: The percentage of categories of incorrect sponses from large models.
We modify the object of triplets in (e) with some perturbation to create uniqueness in the prop- ertics of the artificial entity, but without introducing overly unusual values.
and Boolean questions. The specific distribution of question types is shown in Table 6. Note that we generate only multiple choice questions for the DA question set. This done for two reasons: one is that the mutli-hop questions are too hard for current LLMs as shown in Table 1, and the other that there may be multiple answers to one multi-hop question, and to evaluate the results more accu- rately and avoid false negatives, we utilize choices to limit the answer space.
T ((ēē a, a E Ta(e)} T = porubbbb perrtutbe"e))(ee.... TH(e))
in which perturb() represents a perturbation func- tion that can modify values according to specific needs. For numeric attribute value v, we add gaus- sian noise it. For non-numeric attribute value we use the object the same attribute from sib- lings entities. For association object entity e', we randomly choose one of its siblings as the modificd value.
B.2 Natural Language Question Generation
We prompt ChatGPT to generate question tem- plates given property triplet in one-hop setting or a chain of property triplets multi-hop setting. We provide the prompt generate Boolean, fill-in- the-blank question templates under both settings Figure 4, and 6. We also show some generated question templates in Table 8. To generate multiple choice questions, we simply append four choices to the corresponding fill-inthhebllank questions.
We also include (() to ensure the commonal- ity of entities within the specific class C.
(E) RundomSammlee((éer. v)]
u),, E Ta(e:), Sib(e))
(e) RandonSample(((ãnne)) (c,,r,d) E c E Sib(a)
In order to guarantee the accuracy of the ques- tions generated through this approach, select random sample of 100 questions for both one-hop and multi-hop questions be checked by humans.
The results indicate that over 98% of the one-hop questions and over 95% of the multi-hop questions generated are accurate.
B Dataset Details
B.1 Question Types Distribution
As mentioned in Section 2.4. we divide the AL- CUnA into three subsets. Each subset contains certain number of multiple choice, fill-in-the- the-blank,
C Introduction to Our Model We Select
We choose four representative and popular LLMs. ChatGPT, one of the most powerful models, but is close-source, so we can only call it through the API. The other three are all excellent open-source
We process these four collections in a manner that ensures no overlapping or shared properties between them, therchy climinating any potential contradictions.

---

Relation Chain or Property
Generated Templates
What are the unimals that [T] prey on?
prcy
What animals are preyed on by [T]?
Where is the cingulum located in the (T)'s mouth?
cingulum location
What is the type of cingulum in the teoth of [T]?
How many frost free days required for the growth of [T]?
frost free days
How many frost-free days does the habitat of [TT] have on average?
What is the species that co-roosts with the host [T]?
(have host, co-roost with)
What species shares a roosting habitat with the host of [T]?
What is food source of a species that feeds on flowers visited by an organism parasitized by [T]?
(parasitize, visit flowers of. eat)
What is the food source of the species whose flowers are visited by an organism parasitized by '[T)?
Table 8: Question templates generated from properties of entities or chains of reasoning.
System You are a powerful multi-hop question generator with biological knowledge Users will provide a chain of RDF triples, and you will help write question to ask the tail entity from the head entity. You shouldn't include bridge entities in the generated question. The question should only include the head entity [T], which is placeholder. you can't create valid question, reply with \[[Noee]\" only, Don't reply with any explanation or other information.
1 #exemplars*/
User: Given RDF triples (lT). compete with, x1). (x1, are eaten by, x2). (x2, host of, x3), write question to ask
x3. Don't mention x1, x2, ... Write the possible question natural English.
Agent: Multi-hop question: Which entities serve as the habitats for the creatures that feed on the organisms competing with (T]?
User: Given RDF triples ([T). parasitize, x1), (x1, prey on, x2), write question to ask x2. Don't mention x1, x2,
Write the possible question natural English.
Agent: Multi-hop question: What is the prey of an organism that is parasitized by [T]
User: Given RDF triples ([T), eat, x1), (x1, compete with, x2), (x2, are pathogens of, x3), write a question to ask
x3. Don't mention x1, x2, Write the possible question in natural English.
Agent: Multi-hop question: Which species has pathogens that compete with species that are eaten [T]?
/" "query" /
User: Given RDF triples (STriple chain), write question to ask ($Tail entity). Don't mention x1, x2, Write the possible question in natural English.
Figure 4: Prompt for generating multi-hop question templates.
System: You a powerful question generation model with biological knowledge. Given biological taxon's property name and a set of possible values of the property. You need to generate a yes-no question template with placeholders [T] and [V] about the given property satisfying the following constraints:
1. Most important: the generated question template must contain [T] and [V].
2. The placeholder [T]] can be replaced with any taxons name.
The placeholder [V] be replaced with possible value the given property: You refer the given value set for the format of possible property value. But don't 't use any value in the value set directly.
4. do the replacement, just provide the question template with [T] and [V] placeholders.
property. 5. Try to create the question even if the property is not propetyy....pperty
6. Don't reply with any explanation other information.
1 exemplars *
User: Property name: skeleton contains Value set: ['chitin']
Agent: Question template: Does the skeleton of [T] contain [V]?
User: Property name: body shape Value set: l'cylinder' 'elongated', pilaterallysymmeetii radiallysymmettc
Agent Question template: What's the body shape of [T]
User: Property name: litters per year Value set: ['1.0 /yyar', '3.0 /year', '1.5 /year', '2.0 /year', '2.5 /year']
Agent; Question template: Does one [T] produce [V] litters per year?
/ querr /
User: Property name: (SProperty name) Value set: (SPossible values of Property)
Figure 5: Prompt for generating Boolean question templates.

---

System You are powerful question generation model with biological knowledge. Givena biological taxon's property name. You need to generate a question template with a placeholder [T] ahout the given property so that the placcholder [T]] can be replaced with any taxons name. Try to create the question even the property is not biological property. Don't reply with any explanation other information.
(' exxeplaass* /
User: Property name: skeleton contains
Agent: Question template: Which organic compound is component o [T]'s skeleton?
User: Property name: body shape
Agent: Question template: What's body shape of [T]
User: Property name: litters per year
Agent: Question template: Hlw many litters can [TT] have per year?
/" query" /
User: Property name: (SProperty name]
Figure 6: Prompt for generating fill-i--the-blank question templates.
models. Due to the limitation of GPU memory space, we choose those open-source models rang- ing from 6B-13B.
impact of different training processes on the abil- ity in face new knowledge. We select Llama2- Chat-7B, Alpaca-7B, Vicuna-7B, and ChatGLM- 7B specifically. The results are presented on Table 9.
ChatGPT: ChatGPT is sibling model to In- structGPT (Ouyang et al., 2022), which is trained on a vast instruction dataset and further tuned by reinforcement learning with human feedbacks (RLHF).
D
Introduction to Our Experiment Method
D.1 Zero/Few-shot Evaluation Setting
Alpaca-7B: Alpaca is an open-source inslruction-follovinng LLM trained for academic purposes, which is fine-tuned from the LLaMA 7B model (Touvron et al., 2023) on 52K cestructiionfolloviin demonstrations.
The zero-shot setting is where the model is given explicit instructions to directly complete the mis- sion. This scenario evaluates the ability of the original model to solve the problem autonomously without training. In our benchmark, the input the zero-shot the new knowledge of the artificial entity and a question to be asked about it.
Vicuna-13B: Vicuma is trained by fine-tuning LLaMA using 70K conversations with Chat- GPT shared by users. preliminary evalua- tion using GPT-4 judge shows that Vicuna- 13B achieves more than 90% of the quality of ChatGPT.
Compared to the zero-shot setting, in the few- shot setting the model is given several additional examples from the same task as reference. This allows evaluating the ability of the model to learn the task quickly based on limited number of sam- ples, and is also consistent with practical situations where supervised training is not convenient. Ac- cording to the Min et al. (2022)'s study, in our benchmark assessment, we provided to exam- ples for each type of problem, expecting that it would be sufficient to be able to demonstrate the labeling space for this type of problem.
ChatGLM-6B: ChatGLM is also an open- source instruction-following LLM, which is based on General Language Model (Du et al., 2022) framework.
We download the above three open source models from Huggingface", and thanks to the convenient design of the FastChat? library, we unify the testing framework for all models and call them through the API.
D.2 Chain-of- Chain-of-Thought (CoT) Form
For both the zero-shot and few-shot evaluation set- tings, we add the design of the CoT form. For the zero-shot setting, we added the words "Let's think step by step." the end of the question, expecting the model to output the thinking process, which can help LLM to reason about complex problems. For
We also consider several models with 7B param- eters for evaluation to compare the performance of models same size, which may help to analyze the
h/ttts::/hugiinggace co/ chttps://gittaee ctt. www.///mm//ss.aeeeee hatmm/mmsss//aastteee

---

Llama2-Chat-77 Chat-BB
Alpaca-718 Vicuna-78
ChaiGl
Llama2-Cha-778 Alpaca-71
Vicuna-7B
CharGLM--6
Zero-Shot- Vanilla
Pero-Shot-CoiT
KU
28.92
31.02
28.00
34.64
47.75
39.81
32,74
24.85
32.61
15.35
34.70
32.29
14.29
22.84
KA
18.71
24.60
23.40
10.24
24.61
19.66
17.56
4.97
Avg.
29.57
25.12
30.88
31.26
39.50
29.44
31.67
22.04
Few Nww-Shot-Vanillaa Vanilla
Few-Shot-Chr
KU
37.06
33.80
32.78
47.97
65.20
40.77
38.50
40.91
KD
44.38
38.97
39.39
41.42
44.71
36.24
38.71
37.19
KA
24.42
27.63
24.39
27.47
28.72
25.73
27.11
26.93
Avg.
39.27
35.09
35.14
42.56
50.15
38.02
37.21
37.64
Table 9: Performance LLLMs 7B parameters at our benchmark under different settings.
multiple choice
Boolean
fillin-the-blank
responses, answering multiple options, and other incorrect responses.
KU 2025
3138
3814
KD
2486
24592
12601
We can find that the percentage of answering multiple options for all models is very small, which indicates that all models can understand and com- ply with requirements very well. In addition, some of the questions rejected by some mod- els, probably because some models recognizes that cannot answer the corresponding question and responds with "I don't know" or "I am sorry".
KA 8757
Total
13268
27730
16415
Table 10: Number of different forms of KU, KD and KA questions after filtering.
the few-shot setting, we add the thought process in the answer to each sample question shown to inspire the model.
G Impact of Different Modifications
E Details of Our Approach to Filtering Questions
As shown in Section 5.2, models commonly strug- gle with knowledge differentiation when the arti- ficial entity and the parent entity are similar. In this section, we further conduct ablation study to investigate the specific impact of different modi- fications (i.e. variation and dropout).
Specifically, we retain only those artificial entities whose parent entities could be perfectly recalled by the model. addition, since answering multi-hop questions requires the model to make use of each singlc-hop knowledge, we then filter out any rea- soning chains that contain knowledge that cannot be correctly recalled by the model.
To ablate one type of modification, we recon- struct artificial entities in the KD dataset. For each question in the original KD dataset, have par- entity e and the corresponding attribute a be queried. We then reconstruct several artificial enti- ties by modifying one attribute of cP (except a) a time. From this, we obtain several artificial entities with different similarity caused by the same type of modification. For fair comparison, we only ex- periment with e with 10 attributes. We randomly sample 1000 parent entities and create total of 1000 x 10 10000 new artificial entities. Finally, conduct experiments by querying models about them with the same question about a.
The method used for the above two filtering is construct question templates for the knowl- edge involved, including attributes and relation- ships, based on previous work (Petroni et al., 2019), and then to query the model using the few-shot set- ting. We filter samples in our benchmark for every evaluated model to ensure that our questions are specific to the ability about new knowledge, and then select the intersection of filtered questions for fair experimentation and analysis. The number of questions per category left is shown Table 10.
The experiment results presented in Table Different models exhibit almost the same trend under both modifications. It is clear that dropout yields stable improvement as the similarity de- creases while the impact of variation is relatively weak and insignificant.
F Analysis about Models' Output
An example of the output of the model is shown in Table 1. To better analyze the models' responses, as shown Table 7, we divide all the models' error outputs into 3 categories, including rejecting

---

Question: What organism is parasitized by an organism that feeds on Agabus ajax?
Answer: Rangifer tarandus
Model's Output: "Thought process: Agabus ajax is species of aquatic beetle that feeds on Acari, Culicidae, Daphnia magna, Daphnia pulex, and Ceratopogonidae. To answer the question, we need to identify an organism that is parasitized by an organism that feeds on Agabus ajax.
Final answer: Thala gorii"]
Question: Is Sorghum versinatum type of hyperhydate?
Answer: Yes
Model's Output: "ANSWER: Yes"
Question: What's 's the preferred water depth range for Plana?
Answer: 500.0 cm
Model's Output: ANSWER: 500 emininlt is important to note that the information
Table II: Example of question answer from our models.
Variation
Dropout
Performance
Model
ChatGLM
Vieuna
Alpaca
ChatGPT
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Similarity
Similarity
Figure 7: Relationship between model performance KD questions and the property similarity between the artificial entity and its parental entity due to different levels of modification, i.c variation and dropout.
H Prompts in Experiments
ral knowledge used needs be re-collected each time, requiring labor and resources race with the training of the LLM.
this section, we will show prompts used in our settings. Since our questions contain Boolean, mul-
tiple choice, and fill-in-the-B blank forms, the rest of the prompt is identical except for the restrictions on the output format for each type of question that are targeted. Therefore, without loss of general- ity, we provide the prompts for the fill-in-the-blank questions as demonstration. We present the CoT zero-shot prompt as an example Table 12. The other methods of prompt are basically similar.
2. uncertain validity and risk of information leak- age. Some LLMs do not announce the training data they use, so it is not known whether the temporal knowledge collected each time is still valid or not.
3. fairness of comparison. Since the range of timestamps for training data of each model is different, the new temporal knowledge is dif- ferent for each model. Therefore, the test data used for each evaluation needs be different, and it uncertain whether such a comparison is fair.
I Source of New Knowledge
Temporal Knowledge Using temporal knowl-
edge as source of new knowledge has the follow- ing disadvantages:
In contrast, our proposed KnowGen method solves the above problems. Since it is artificial knowledge, it will be valid for a long time and no
1. the expense of collecting data. With the con- tinuous emergence of new LLMs, the tempo-

---

You are a powerful
system with knowledge in the field of biology.
Users will provide some biological information along with question. Your task to combine the information provided by the user with your biological knowledge to answer the question.
If you are unable to answer the question, simply respond with "I don't know."
Here is the basic information about a taxon can refer: ###
"name": "Bainvillevillea spinosa".
"property": {
"cellularity": ("multicellular"),
"conservation status": ["least concern]].
"geographic distribution": ["Ecuador"],
"habitat": ["terrestrial"],
"leaf complexity": ("compound"],
"leaf morphology": ["broad"],
"leaf sheddability":: ["evergreen"),
"plant growth form": ("branched"),
"produces": ["oxygen"]]
"woodiness"; ["woody"]
"rank": "species"
###
Answer the following question few words: What is the habitat of Bainvillevillea spinosa? Desired format: Thought process: <Thought process>, Final answer: [Final answer]. Let's think step by step.
Table 12: Demonstration of the zero-shot prompt in the CoT form.

---

more effort is required collect data repeatedly. Moreover, for all models, the knowledge defi- nitely new, so we can also use the same test data to evaluate the models, which also ensures the validity and fairness of the evaluation.
Previous methods of entity and attribute substi- tutions As for the relationship previous work (Longpre al., 2022; Zhou et al., 2023), all of us "construct" knowledge, but our knowledge forms, construction purposes, and implementation meth- ods are different.
1. Knowledge form: The knowledge in Longpre et al. (2022) and Zhou et al. (2023) a state- ment of a few sentences describing a simple fact. Whereas our knowledge is represented in ontological form, with complete properties, relations and classes, which is more structured and complete.
2. Purpose of construction: both of them are de- signed to construct knowledge that contradicts the internal knowledge of the model, way to make the model hallucinatory or unreliable predictions. Instead, our work intended to simulate the generation of new knowledge that is associated and consistent with the existing knowledge.
3. Implementation: the methods of them are lim- ited by the form of knowledge representation, and merely construct counterfactuals by re- placing the name of the entity in the sentence with the name of another existing entity. Our method, on the other hand, will create com- pletely new entity with new name and which reasonably exists the original knowledge system through operations such as heredity, variation and dropout.


Reviews:
[{"review_id": "LR401nQUB0", "reviewer": "Reviewer_wiV6", "summary": "", "questions": "Question A: What is the naming scheme for new entities for the overall experiments ? There is an experiment showing the effect of naming scheme, but which one is chosen for the rest of the experiments?\n\nQuestion B: Line 373 - What does the “number of Google search terms” criteria used for identifying specialized entities mean?\n\nQuestion C: For the experiment in Fig 3 - can you keep same entities but vary the number of properties that are inherited/varied during construction? I think this would provide a more careful analysis of the influence of similarity.", "limitations": "", "rating": 4, "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 careful and valuable comments. We will explain your concerns point by point.\n> 1. Performance of models on existing knowledge - the baseline of how these models perform on reasoning about organisms in general is important establish the drop in performance with new knowledge... (Reject 1.i)\n\n**A1**: Thank you for your thoughtful advice. \n\nFirst, we would like to clarify that we have already filtered all multi-hop questions to ensure that existing knowledge involved in each single-hop can be recalled by the model as described in Appendix E (line 978-982). \n\nSecond, we have in fact conducted a similar experiment to explore the reasons for the lower performance on KA in Section 6.3, Chain Entities are Key to Knowledge Association (line 588-590). We found that including all problem-related knowledge (i.e. all chain entities including both artificial and existing entities) in the context leads to performance improvement in KA. To some extent, it shows that the main problem is the inability of LLMs to make the association between new knowledge (provided in context) and existing knowledge (recalled from model parameters), rather than a lack of reasoning ability.\n\nWe totally agree with you about the baseline of reasoning about existing entities, which eliminates the influence from artificial entities.Here we report the results in the table below. For each multi-hop question in KA about artificial entity $\\tilde e$ with triplets chain $\\mathcal{T}_C = (\\tilde e, r_1, e_1)\\rightarrow ...  $, we utilize the parent entity $e^p$ with the same relation value $(e^p, r_1, e_1)$ to construct another triplets chain $\\mathcal{T}_C' = (e^p, r_1, e_1)\\rightarrow ...  $, and create a multi-hop question about existing knowledge only. \n\n|            | questions about artificial entities | questions about existing entities |  \n|------------|--------|--------|  \n| alpaca-7b  | 27.72  | 39.20  |  \n| vicuna-13b | 29.53  | 53.13  |  \n| chatglm    | 27.76  | 59.76  |  \n| chatgpt    | 41.46  |  86.82  |  \n\nAs shown in the table, there is a large performance gap between the two experiment settings, which demonstrates that the reason models struggle with KA is due to the influence of new knowledge. \n\n> 2. Performance across modifications - Fig 3 shows performance across entity similarity. Extending such analysis to different levels of modification (heriditary v/s variation v/s dropout), ablating one modification type at a time would help understand what kinds of knowledge change models struggle with. (Reject 1.ii, Question C)\n\n**A2**: Thank you for your advice. To avoid misunderstanding, we firstly revise that questions in KD set only relates to the attributes of variation and dropout, and the similarity is in fact measured by the proportion of heredity.\n\nHere we reconstruct artificial entities in the KD dataset to analyse the influence of different modification (dropout or variation). For each question in the original KD dataset, we have a parent entity $e^p$ and the corresponding attribute $a$ to be queried. We then construct several artificial entities by modifying one attribute of $e^p$ (except $a$) at a time. From this, we obtain several artificial entities with different similarity caused by the same type of modification. For a fair comparison, we only experiment with $e^p$ with a fixed number of attributes (10 in specific). We randomly sample 1000 parent entities and create 1000 * 10 = 10000 new artificial entities. The result is shown below.\n\n***Variation***\n  \n|   similarity    | 0   | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 |  \n|-------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|  \n| ChatGLM |0.36|0.39|0.37|0.39|0.39|0.39|0.4|0.4|0.39|0.38|\n| Vicuna  |0.43|0.42|0.41|0.41|0.41|0.4|0.38|0.37|0.38|0.35|\n| Alpaca  | 0.34|0.37|0.36|0.35|0.33|0.33|0.32|0.32|0.34|0.36|\n| ChatGPT |0.571|0.559|0.566|0.567|0.576|0.579|0.579|0.574|0.571|0.566|\n\n  \n***Dropout***\n\n| similarity      | 0   | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 |  \n|-------|-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|  \n| ChatGLM |0.47|0.46|0.47|0.49|0.48|0.41|0.44|0.42|0.41|0.38|\n| Vicuna  |0.57|0.51|0.53|0.51|0.49|0.46|0.42|0.38|0.39|0.35|\n| Alpaca  |0.42|0.39|0.38|0.34|0.35|0.35|0.35|0.34|0.35|0.36|\n| ChatGPT | 0.647|0.659|0.654|0.667|0.638|0.61|0.602|0.595|0.593|0.564|\n\n\nResults on different models exhibit almost the same trend. It is shown that dropout yields a stable improvement as the similarity decreases while the effect of variation is relatively weak and insignificant.\n\n\n> 3. Comparability across models - as the data for evaluation for each model is different (filtering Sec 5.4) - are the results comparable across models? Experimenting with entities new to all models might help address this concern. (Reject 2)\n\n**A3**: Yes, we totally agree with you. In fact **that's exactly what we do**! A detailed description of how we filtered the data is shown in Appendix E (line 988-992): \n \"*We filter samples in our benchmark for every evaluated model to ensure that our questions are specific to the ability about new knowledge, and then select the **intersection** of filtered questions for fair experimentation and analysis*.\"\n\nWe are very sorry for the confusion caused by our lack of clarity.\n\n>  4. What is the naming scheme for new entities for the overall experiments ? There is an experiment showing the effect of naming scheme, but which one is chosen for the rest of the experiments? (Question A)\n\n**A4**: We apologize for commenting out the description of the entity naming scheme in our experiment due to a typo.\nThe reason we use SIMILAR and RANDOM naming scheme for the experiment in Table2 is that we want to reflect the effect of the name on the model more clearly through these two extreme naming schemes. \n\nIn the real world, when a new creature is found, the naming scheme is usually to use the same prefix or suffix as creatures of its own species.\n\nIn other experiments, **in order to mimic the real world scenario** and considering the tokenization algorithms used in LLMs, we firstly split the names of related existing entities (parent entity and sibling entities of parent entity) into **subwords** through one tokenizer (WordPiece tokenizer). Then we randomly select the name of related entities, and **for the i-th selected entity we choose its i-th subword**. For example, \"ALCUNA\" is created from **AL**PACA and VI**CUNA**.\n\nSorry again for our typo. This experiment setting is important to state, and we will add it in the camera ready version. Thank you very much for your careful review of our paper.\n\n> 5. Line 373 - What does the “number of Google search terms” criteria used for identifying specialized entities mean? (Question B)\n\n**A5**: Since the EOL dataset contains almost all of the world's organisms, some of them may be quite rustic and cannot be recognized by almost all models in our preliminaries. With the same purpose as the filtering of the data in Section 5.4, we first use Google search to determine whether an entity is common or not. For these entities that we get less than 10K search results, we discard them. In this way, we obtain an initial filtered list of common entities. Then we use models to filter the entities left to ensure that these entities are known by them as described in Section 5.4.\n**In short, Google search is to get more common entities, which is a preliminary operation prior to model filtering.**", "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. Clever, simple and scalable method to construct artificial benchmark of new knowledge.\n2. New entities are not entirely random or non-sensical, but are connected to part of existing ontology. The entity construction algorithm takes commonalities and variations in classes into account to ensure new entities are similar in some extent to existing entities. This helps truly test the generalization to new entities of similar type.\n3. Constructed new knowledge is controllable to allow varying of overlap with existing knowledge.\n4. Experiments are detailed - studying the LLM’s abilities via zero and few shot prompting and by varying overlap, modifying names, etc. Experiments and findings go deeper to provide few hypotheses for the model behaviour (entity confusion)", "reasons_to_reject": "1. While the paper has many experiments, there are a couple of experiments which I think are important to solidify the findings:\n\n\ti) Performance of models on existing knowledge - the baseline of how these models perform on reasoning about organisms in general is important establish the drop in performance with new knowledge. This would answer whether the model struggles with KA in general when queried about organisms or whether this is specific to new knowledge. If models struggle with existing knowledge, the issue could be the usage of structured knowledge from the input rather than using “new” knowledge.\n\n\tii) Performance across modifications - Fig 3 shows performance across entity similarity. Extending such analysis to different levels of modification (heriditary v/s variation v/s dropout), ablating one modification type at a time would help understand what kinds of knowledge change models struggle with.\n \n2. Comparability across models - as the data for evaluation for each model is different (filtering Sec 5.4) - are the results comparable across models ? Experimenting with entities new to all models might help address this concern.\n\nIf the experiments and clarifications are added to the paper, I'm willing to increase my score.", "questions_to_authors": "Question A: What is the naming scheme for new entities for the overall experiments ? There is an experiment showing the effect of naming scheme, but which one is chosen for the rest of the experiments?\n\nQuestion B: Line 373 - What does the “number of Google search terms” criteria used for identifying specialized entities mean?\n\nQuestion C: For the experiment in Fig 3 - can you keep same entities but vary the number of properties that are inherited/varied during construction? I think this would provide a more careful analysis of the influence of similarity.", "paper_topic_and_main_contributions": "The paper aims to evaluate the ability of current LLMs to reason in the presence of new knowledge (concepts that are different or new from existing corpora). For this, they first propose a method to construct new entities that are similar to existing entities in a structured knowledge but different in some properties. Using Encyclopedia of Life as the structured knowledge they construct 3k such new entities. Using prompting mechanisms, they generate questions (via templates) to probe different models. Finally they study 4 LLMs and present findings about the ability of LLMs to answer questions about new entities and new knowledge. \n\nThis is an important problem to study, especially in context of applying models to changing knowledge. Interestingly, their task formulation differs from prior studies on temporal new knowledge (which are facts not available to the model during training, but are often of similar of existing entities and potentially have information leakage). The formulation here tests models on the ability to process and generalize to completely new entities which are still grounded to existing knowledge. Their main contributions is a clever method to generate new knowledge, which is connected to existing knowledge (is similar in many ways) but is still sufficiently new. This would help study the ability of models on new knowledge, without performing such evaluation in real time. The benchmark would be useful in addition to test knowledge editing and propagation methods like [1, 2], to test how extensively models use new knowledge.\n\nThey evaluate LLMs by providing new knowledge as part of the input via prompting. Their experimental findings suggest that while ChatGPT is able to answer simple questions on new knowledge, it still struggles with more complex reasoning. All other models (smaller model sizes) struggle with simple and complex questions. The findings are interesting and were important to experimentally establish. They highlight that injecting knowledge of new entities might not be easily achievable via simple prompting or providing the knowledge as part of input. \n\n[1] De Cao et, al 2021 - Editing Factual Knowledge in Language Models\n\n[2] Onoe et, al 2023 - Can LMs Learn New Entities from Descriptions? Challenges in Propagating Injected Knowledge", "excitement": 4, "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": {}}, {"review_id": "LXrb2bNCNt", "reviewer": "Reviewer_NN51", "summary": "", "questions": "**Question A**: Why is a hybrid entity based evaluation benchmark (ALCUNA) preferable over evaluation of new entities from a temporal KBs (such as wikipedia based examples mentioned earlier). \n\n**Question B**: Can the hybrid parent + sibling mechanism account for completely new knowledge relating to new classes being discovered? (eg. a new family/species)\n\n**Question C**: Why is the few-shot CoT performance of models for the knowledge association task (Table 1) significantly worse? Excluding this row, few-shot CoT numbers  seems to always improve performance.", "limitations": "", "rating": 4, "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 careful and valuable comments. We will explain your concerns point by point.\n> 1. Why is a hybrid entity based evaluation benchmark (ALCUNA) preferable over evaluation of new entities from a temporal KBs (such as wikipedia based examples mentioned earlier). (Question A)\n\n**A1**: This is a good question, thank you for raising it. We also mention the use of temporal knowledge (i.e., using the most updated knowledge after the model has been trained) to evaluate the model in lines 83-88 of the paper. However, there are the following drawbacks:\n1) **the expense of collecting data**. With the continuous emergence of new LLMs, the temporal knowledge used needs to be re-collected each time, requiring labor and resources to race with the training of the LLM.\n2) **uncertain validity and risk of information leakage**. Some LLMs do not announce the training data they use, so it is not known whether the temporal knowledge collected each time is still valid or not.\n3) **Fairness of comparison**. Since the range of timestamps for training data of each model is different, the new temporal knowledge is different for each model. Therefore, the test data used for each evaluation needs to be different, and it is uncertain whether such a comparison is fair.\n\nIn contrast, our proposed KnowGen method solves the above problems. Since it is artificial knowledge, it will be valid for a long time and no more effort is required to collect data repeatedly. Moreover, for all models, the knowledge is definitely new, so we can also use the same test data to evaluate the models, which also ensures the validity and fairness of the evaluation.\n\nReviewer wiV6 also considers our task formulation to be important and proposed method to be valuable:\n*This is an **important problem** to study, especially in context of applying models to changing knowledge. Interestingly, their task formulation **differs from prior studies on temporal new knowledge (which are facts not available to the model during training, but are often of similar of existing entities and potentially have information leakage)**...Their main contributions is a clever method to generate new knowledge, which is connected to existing knowledge... This would help study the ability of models on new knowledge, **without performing such evaluation in real time***. \n\n> 2. Can the hybrid parent + sibling mechanism account for completely new knowledge relating to new classes being discovered? (eg. a new family/species) (Question B)\n\n**A2**: First, it needs to be clarified that our aim is to **evaluate LLMs by creating reasonable new knowledge** as mentioned in Q1, not to simulate the emergence of all possible new knowledge, which is impossible because the emergence of new knowledge is a process of human cognition and the development of things.\n\nSecond, the emergence of a completely new class of knowledge is rare, and if it occurs, it is perfectly fine to use it directly to test LLMs.\nIn fact, our contribution is the bulk creation of reasonably new knowledge that is meant to solve the problem of not having suitable new knowledge for evaluating big models. **The emergence of entirely new knowledge is nice, and our work does not need to cover all the generation of new knowledge.**\n\n> 3. Why is the few-shot CoT performance of models for the knowledge association task (Table 1) significantly worse? Excluding this row, few-shot CoT numbers seems to always improve performance. (Question C)\n\n**A3**: We have also observed this strange phenomenon. To further explore the reasons behind it, we sampled 100 questions for analysis. We have observed several failure patterns in the model outputs.\n\nFor large-scale LLM (ChatGPT):\n\n1) Under CoT setting, model is more inclined to output \"I don't know\" than under Vanilla setting (58 samples v.s. 25 samples). We find that in the corresponding rationales, ChatGPT usually comes to an intermediate conclusion of \"Relevant information is not provided in the context\", rather than reasoning with their internal prior knowledge.\n\n\nFor small-scale LLMs (ChatGLM, Vicuna, Alpaca):\n\n1) The rationales in model outputs under CoT setting only repeat information provided in the prompt, which only relates to the first hop reasoning. It doesn't help much with multi-hop reasoning in the KA scenario.\n\n2) LLMs under CoT setting often output failure answer 0 in a fixed string format, *\"... is not specified. Therefore, the answer is 0.\"*. The pattern occurs 15 times in 100 samples. This phenomenon is similar to the first failure pattern of large-scale LLMs mentioned above.\n\nWe list one sample for each failure pattern in the table below.\n|Question|Answer|Vanilla Output|CoT Output|\n|---|---|---|---|\n|What plant species grows on the organisms that are eaten by Hyppa recta?\\n0. Chlorocebus pygerythrus\\n1. Abaria electa\\n2. Sus scrofa\\n3. Galium aparine|3|ANSWER: 3 (Galium aparine)|None of the organisms listed are eaten by \\'Hyppa recta\\'. The organisms that are eaten by \\'Hyppa recta\\' are \\'Salvia officinalis\\', \\'Vaccinium\\', and \\'Vaccinium angustifolium\\'. There is no information available to suggest that any of these plants grow on any of the organisms listed. Therefore, the ANSWER is \"I don\\'t know.\"|\n|What is the host of the organism that is preyed upon by the parasite 'Eucharisaris intermedia'?\\n0. Separatista\\n1. Thomisidae\\n2. Anura\\n3. Zale aeruginosa|1|ANSWER:1|The organism that is preyed upon by the parasite 'Eucharisaris intermedia' is 'Formica fusca' or 'Formica'. Therefore, the ANSWER is [2]|\n|What is the organism parasitized by the fourth-level host of 'Pterocarparpusinalis'?\\n0. Pompilus fasciatus\\n1. Quercus robur\\n2. Houstonia palmeri\\n3. Amelanchier|1|ANSWER:1|The organism parasitized by the fourth-level host of \\'Pterocarparpusinalis\\' is not specified. Therefore, the ANSWER is \"0\".|\n\nWe think this phenomenon is interesting and worth exploring further.\n\n> 4. Unclear generalizability of method for generating new entity/relations. While the EOL biological dataset provides a combinatorially large space for new entity generation, it is restricted to a specific domain. If is unclear if an ALCUNA like method would work for alternative KBs. The authors claim in the limitations that structured data is only available in the biological domain, but this is not the case (WordNet/ConceptNet/Wikidata etc). (Reject 2)\n\n**A4**: It needs to be clarified that in the Limitations section, \"we have implemented it only on biological data due to to the factor of structured datasets\"(line 635-637) is trying to convey that since EOL data fits well into the ontological organization, we have only created the ALCUNA benchmark via the KnowGen method as a specific implementation. \n\nBut as we said earlier, \"the method we design can be used for the construction of any knowledge that satisfies the ontological representation\"(line 633-635). It is absolutely possible to use this approach to create new knowledge in other domains for evaluating LLMs. **It is because KnowGen method for creating new knowledge only requires some defined classes, and some entities in the classes, entities with their own attributes, and connections between the entities**. Any knowledge base with such a structure can be used to create new knowledge with our KnowGen method. Thus our method is generalizable.\n\nFor example, Wikidata is also ontological [1]. There are many items in Wikidata, which have different properties and relations.\nIn addition, items have two special properties: *instance of* (P31) and *subclass of* (P279) which can be used as *Class* in our paper. So Wikidata can also be used to create new knowledge by KnowGen.\n\n> 5. An important evaluation measure missing from the experiments is the performance of the chosen models on entities that already exist within EOL. Having these numbers would help contextualize the drop in performance for new entities. (Reject 3)\n\n**A5**: It is possible that our lack of emphasis has led to your misunderstanding. We would like to clarify that \"we retained only the artificial entities whose parent entities could be recalled by the models perfectly\" (line 976). You can refer to Section 5.4 and Appendix E for the details about filtering. In other words, **the performance of chosen models on the filtered existing entities within EOL is one hundred percent correct**. Therefore, we eliminate the influence of unknown exisiting entities (since none of them exists) to independently analyze the impact of new knowledge, and hence \"compare the models’ performance in face of new knowledge in a more focused and fair way\"(line 476).\n\n> 6. Typos and missing references. (Reject 1, Typos)\n\n**A6**: Thank you for your patience and careful review, we will fix these typos.\nIn addition, utilizing temporal knowledge as new knowledge for evaluating models is indeed a relevant kind of solution, so we will add these papers to the related work section in our camera-ready version.\nWe apologize for our mistake and thank you again for your suggestions and corrections.\n\n[1] Wikidata: A large-scale collaborative ontological medical database", "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.  A straightforward mechanism to create synthetic, algorithmically generated benchmark could help address the issue of evaluation for LLMs. This mechanism depends upon a pre-built knowledge base, and could potentially be applied to other domains (beyond the biological species).\n2. The error analysis centered around entity overlap, chain entities, and the role of structured representation provides a good example of mechanisms to check the robustness of central claims of the performance of LLMs on new knowledge.", "reasons_to_reject": "1. A lack of sufficient coverage of related work, especially on the effects of new temporal behavior of LMs (Lazaridou et al;, 2021; Zaporojects et al., 2022; Jang et al., 2022; Agarwal and Nenkova, 2022). In particular, Jang et al (2022) generate Wikipedia diffs over time and these could be an alternative mechanism for evaluating new knowledge in LLMs (assuming an appropriate prompting setup).\n\n2.  Unclear generalizability of method for generating new entity/relations. While the EOL biological dataset provides a combinatorially large space for new entity generation, it is restricted to a specific domain. If is unclear if an ALCUNA like method would work for alternative KBs. The authors claim in the limitations that structured data is only available in the biological domain, but this is not the case (WordNet/ConceptNet/Wikidata etc).\n\n3. Lack of baseline EOL entity QA performance: An important evaluation measure missing from the experiments is the performance of the chosen models on entities that already exist within EOL. Having these numbers would help contextualize the drop in performance for new entities.", "questions_to_authors": "**Question A**: Why is a hybrid entity based evaluation benchmark (ALCUNA) preferable over evaluation of new entities from a temporal KBs (such as wikipedia based examples mentioned earlier). \n\n**Question B**: Can the hybrid parent + sibling mechanism account for completely new knowledge relating to new classes being discovered? (eg. a new family/species)\n\n**Question C**: Why is the few-shot CoT performance of models for the knowledge association task (Table 1) significantly worse? Excluding this row, few-shot CoT numbers  seems to always improve performance.", "paper_topic_and_main_contributions": "This paper describes a method to generate structured datasets which can be used to evaluate the ability of LLMs on unseen (at training time) knowledge.  The authors start from the Encyclopedia of Life (EOL) - an existing structured knowledge base consisting of attributes for and relations between different biological entities. They then propose a mechanism to construct hybrid novel entities with attributes and relations derived from a single parent and multiple siblings, all belonging to the same class.  They then provide a structured representation of this entity as input to an LLM, and evaluate it on question answering for this entities on single-hop or multi-hop reasoning questions. The questions used are based on templates derived from ChatGPT. The evaluation results are provided across 4 LLMs, mainly providing evidence of characteristics of entities that influence exact-match QA performance across zero-shot, few-shot and chain-of-thought setups.\n\n**Contributions:**\n\n- A benchmark and method to test LLMs for ability to reason about unseen knowledge.\n- Error analysis indicating the properties of novel entities and answering strategies that maximize the QA exact answer performance.", "excitement": 4, "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "line 216: We → we\n\nline 271 “mimic human progressive cognitive process of entities” a verb is missing here?"}, "extra_text": {"Missing_References": "Lazaridou, Angeliki, et al. \"Mind the gap: Assessing temporal generalization in neural language models.\" *NeurIPS 2021.*\n\nZaporojets, Klim, et al. \"TempEL: Linking dynamically evolving and newly emerging entities.\" *NeurIPS 2022.*\n\nJang, Joel, et al. \"TemporalWiki: A lifelong benchmark for training and evaluating ever-evolving language models.\" EMNLP 2022.\n\nAgarwal, Oshin, and Ani Nenkova. \"Temporal effects on pre-trained models for language processing tasks.\" *TACL 2022.*"}}, {"review_id": "xjYpJrsqlI", "reviewer": "Reviewer_Hv6T", "summary": "", "questions": "1. Can I get a detailed example of the model's input concerning a specific query? Which related knowledge triplets are presented to the models along with the queries? Is additional knowledge about existing entities also provided?\n2. Could you clarify what the KD and KA scores imply? In my view, if a model exhibits insufficient performance on knowledge differentiation, it might be due to inadequate understanding of the new knowledge or confusion brought on by existing knowledge about familiar entities. If a model performs poorly on KA, it might be because of the model's limited ability to execute multi-step reasoning or a lack of memorization of the single-hop relations within the multi-step chain.", "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": "Thanks for your careful and valuable comments. We will explain your concerns point by point.\n\n> 1. The evaluation scope is relatively limited, with just 4 LLMs being tested. These models vary in size (Alpaca is 13B, Vicuna is 7B), resulting in an insufficiently holistic outcome and potentially unfair comparisons. (Reject 1)\n\n**A1**: Thank you for your advice. Considering the computational cost as well as the length of the paper, we evaluate several popular LLMs (Alpaca-7B, Vicuna-13B, ChatGLM-6B and ChatGPT) and focus on detailed analysis experiments. \nIn fact, it is very hard for model evaluation work to evaluate a large number of large models nowadays due to computational cost and other reasons[1]. For example, AGIEval[2] evaluates only 3 models and Open-QA[3] evaluates only 4 models.\n\nWe will later release our dataset to make it easy for the community to evaluate other models using our benchmarks. As more and more LLMs continue to emerge, a comprehensive evaluation of current models requires a joint effort by the entire community.\n\nIn addition, following your suggestion, we have supplemented our experiments on models of the same size.\nUnlike the previous small models such as BERT, it is difficult to freely choose models of the same size for evaluation because the large models are now published by different organizations and each model is far more expensive to train than before (Alpaca, for example, does not have an official 13B version). Based on these considerations, we select Vicuna-7B[4] as well as Llama2-Chat-7B[5] (an impressive model that has recently released) for evaluation as supplementary experiments.\nThe results are shown in the following table. \n\n|      | Vicuna-7B         | Vicuna-7B     | Vicuna-7B        | Vicuna-7B    | Llama2-7B-Chat    | Llama2-7B-Chat | Llama2-7B-Chat   | Llama2-7B-Chat |\n| ---- | ----------------- | ------------- | ---------------- | ------------ | ----------------- | -------------- | ---------------- | -------------- |\n|      | Zero-Shot-Vanilla | Zero-Shot-CoT | Few-Shot-Vanilla | Few-Shot-CoT | Zero-Shot-Vanilla | Zero-Shot-CoT  | Few-Shot-Vanilla | Few-Shot-CoT   |\n| KU   | 28.00             | 32.74         | 32.78            | 38.50        | 28.92             | 47.75          | 37.06            | 65.20          |\n| KD   | 34.70             | 33.61         | 39.39            | 38.71        | 32.61             | 37.48          | 44.38            | 44.71          |\n| KA   | 23.40             | 17.56         | 24.39            | 27.11        | 18.71             | 24.61          | 24.42            | 28.72          |\n| Avg. | 30.88             | 31.67         | 35.14            | 37.21        | 29.57             | 39.50          | 39.27            | 50.15          |\n\nCombined with Table 1 in the paper, we can see that Vicuna-7B generally performs worse than the Vicuna-13B, but performs better than the Alpaca-7B most of the time, and performs similarly to the ChatGLM-6B.\nAnd the Llama2-Chat-7B performs better than the Vicuna-7B. The most impressive thing is the much improved results under Few-Shot-CoT. This suggests that Llama2-Chat has a stronger reasoning ability, which may be due to its higher quality instruction data. The above findings are also consistent with the technical report on Llama2[5], which states that it performs the best among the open source models on reasoning tasks.\n\n**With more and more large models emerging, model evaluation is a huge undertaking. Our contribution in this paper is in designing methods to create new knowledge and in implementing the ALCUNA benchmark**. We call for a joint community effort to evaluate the ability of large models to meet new knowledge on our benchmarks, to form a well-developed leaderboard.\n\n\n> 2. Can I get a detailed example of the model's input concerning a specific query? Which related knowledge triplets are presented to the models along with the queries? Is additional knowledge about existing entities also provided? (Question 1; Reject 2)\n\n**A2**: Sorry for the inconvenience caused to you. **In fact, we have provided examples of the dataset as well as examples of the inputs to the model in the paper**.\nAs mentioned in Section 5.2, \"an example of prompt used in our experiment is shown in Table 11\" (in line 477-478).\nAnd we also metion it in Appendix G: \"We present the CoT zero-shot prompt as an example in Table 11. The other methods of prompt are similar\" (in line 1016-1018).\n**An example of our dataset is also shown in Table 11, from which it can be seen that we do not provide existing knowledge as well as related knowledge.**\n\nThe reason we do not provide them is because of the filtering we have performed in Section 5.4, which ensures that all the existing knowledge involved in our questions can be perfectly recalled by models. Therefore, there is no need to provide them and it puts the focus of our evaluation on new knowledge.\n\nMoreover, **we have also performed the comparison experiments that provide parent entities and related reasoning knowledge**, see Section 6.3. Through these experiments we have come to some conclusions: 1) Parent Entity Aggravates Confusion; 2) Chain Entities are Key to Knowledge Association; 3) Structured Knowledge is Better.\n\nOur full dataset will be released in the future.\nIf you want more examples now, here is another example of input of our models with the setting few-shot prompt (to make it easier to read, we use \"...\" to denote excessive properties and ):\n\n```json\n\"instruction\": \"\"\"You are a powerful question-answering system with knowledge in the field of biology. Users will provide some biological information along with a question. Your task is to combine the information provided by the user with your knowledge of biology to answer the question. If you are unable to answer the question, simply respond with \"I don't know.\"\n\"\"\",\n\"exemplars\": [\n      [\"\"\"Example 0: Based on you encyclopedic biological knowledge, answer the following question a few words: Is Ecuador part of the geographic distribution of Bainvillevillea spinosa? Reply with: \"Yes\" or \"No\". \n  Here is the basic information about a taxon you can refer: \n  ###\n  {\n    \"name\": \"Bainvillevillea spinosa\",\n    \"property\": {\n      \"geographic distribution\": [\"Ecuador\"],\n      \"habitat\": [\"terrestrial\"],\n      \"leaf complexity\": [\"compound\"],\n      \"leaf morphology\": [\"broad\"],\n      \"leaf sheddability\": [\"evergreen\"],\n      ...\n      \"produces\": [\"oxygen\"],\n      \"woodiness\": [\"woody\"]\n    },\n    \"rank\": \"species\"\n  }\n  ###\n        \"ANSWER: Yes\"\n  \"\"\"\n   ],\n      [\"\"\"Example 1: Based on you encyclopedic biological knowledge, answer the following question a few words: Is Philendron xanatii found in the geographic distribution of Panama? Reply with: \"Yes\" or \"No\". \n  Here is the basic information about a taxon you can refer: \n  ###\n  {\n    \"name\": \"Philendron xanatii\",\n    \"property\": {\n      \"geographic distribution\": [\"Ecuador\"],\n      \"plant growth form\": [\"siphonous\"],\n      ...\n      \"produces\": [\"oxygen\"],\n      \"trophic guild\": [\"photoautotroph\"]\n    },\n    \"rank\": \"species\"\n  }\n  ###\n        \"ANSWER: No\"\n  \"\"\"\n  ],\n      [\"\"\"Example 2: Based on you encyclopedic biological knowledge, answer the following question a few words: Is Burma part of the geographic distribution of Curculigoigochioides? Reply with: \"Yes\" or \"No\". \n  Here is the basic information about a taxon you can refer: \n  ###\n  {\n    \"name\": \"Curculigoigochioides\",\n    \"property\": {\n      \"cellularity\": [\"aggregative multicellular\"],\n      ...\n      \"produces\": [\"oxygen\"],\n      \"trophic guild\": [\"photoautotroph\"]\n    },\n    \"rank\": \"species\"\n  }\n  ###\n        \"ANSWER: I don't know\"\n  \"\"\"\n  ]\n],\n\"query\": \"\"\"Based on you encyclopedic biological knowledge, answer the following question a few words: Does Eos squamata visit Casuarina grandis? Reply with: \"Yes\" or \"No\". \n  Here is the basic information about a taxon you can refer: \n  ###\n  {\n    \"name\": \"Eos squamata\",\n    \"property\": {\n      \"Body symmetry\": [\"bilaterally symmetric\"],\n      \"ecoregion\": [\"Seram Rain Forests\"],\n      \"life span\": [\"15.2 years\"],\n      ...\n      \"visit\": [\"Casuarina grandis\"],\n      \"visual system\": [\"corneal eyes\"]\n    },\n    \"rank\": \"species\"\n  }\n  ###\n  \"ANSWER:\"\n  \"\"\"\n```\n\n\n> 3. Could you clarify what the KD and KA scores imply? In my view, if a model exhibits insufficient performance on knowledge differentiation, it might be due to inadequate understanding of the new knowledge or confusion brought on by existing knowledge about familiar entities. If a model performs poorly on KA, it might be because of the model's limited ability to execute multi-step reasoning or a lack of memorization of the single-hop relations within the multi-step chain. (Question2; Reject 3)\n\n**A3**: Thank you for the opportunity to clarify this point. First, it is important to note that **KU, KD, and KA are the three capabilities that we define and that we design tasks to assess**. As described in lines 104-112 of the paper, when the LLM is faced with new knowledge, KU refers to the model's ability to understand the new knowledge, KD refers to the model's ability to distinguish between new and existing knowledge, and KA refers to the model's ability to associate the new and existing knowledge. These three capabilities are well understood and self-consistent.\n\nSecond, if it is really desirable to split these abilities, then it is important to note that in our experiments, as described in Section 5.4 and Appendix E, **we have performed data filtering**. This means that the **model can perfectly memorize existing knowledge** now. So **both KD and KA are abilities concerning new knowledge**, i.e., KD performs poorly because of confusion between new and existing knowledge, and KA performs poorly because of the model's poor reasoning ability on new and existing knowledge.\nIn addition, the experiments in Figure 3, Table 3 and Table 4 may answer your confusion. Figure 3 illustrates that the more similar the new and existing knowledge are, the worse the performance on KD. This suggests that the poor performance on KD is due to **confusion**. Table 3 illustrates that when existing knowledge is provided in context, the model performs worse on KD, which also suggests that existing knowledge causes confusion. Table 4 illustrates that the model's performance on KA improves only to a limited extent when single-hop relations in reasoning chains are provided in the context, which suggests that **the model is mainly limited by the poor ability to combine new and existing knowledge for reasoning**.\n\n\n[1] Chang, Yupeng, et al. \"A survey on evaluation of large language models.\" arXiv preprint arXiv:2307.03109 (2023).\n\n[2] Zhong, Wanjun, et al. \"Agieval: A human-centric benchmark for evaluating foundation models.\" arXiv preprint arXiv:2304.06364 (2023).\n\n[3] Wang, Cunxiang, et al. \"Evaluating open question answering evaluation.\" arXiv preprint arXiv:2305.12421 (2023).\n\n[4] Chiang, Wei-Lin, et al. \"Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90\\%* ChatGPT Quality\"\n\n[5] Touvron, Hugo, et al. \"Llama 2: Open foundation and fine-tuned chat models.\" arXiv preprint arXiv:2307.09288 (2023).", "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. Testing LLM on new knowledge is an important topic, and this paper proposes an effective dataset construction method to facilitate this.\n2. The demonstration figure 1 is concise and straightforward.", "reasons_to_reject": "1. The evaluation scope is relatively limited, with just 4 LLMs being tested. These models vary in size (Alpaca is 13B, Vicuna is 7B), resulting in an insufficiently holistic outcome and potentially unfair comparisons.\n2. The authors do not provide any complete example from the dataset, and the dataset files are absent from the supplementary materials. Thus, the precise nature of the input/output for the LLMs remains vague. (Refer to question 1)\n3. The scores on KD, KA are coupled with multiple capabilities, including comprehension of new knowledge, memorization of existing knowledge, and knowledge reasoning. It is hard to interpret from the current scores and reach conclusions regarding the model's capabilities. (Refer to question 2)", "questions_to_authors": "1. Can I get a detailed example of the model's input concerning a specific query? Which related knowledge triplets are presented to the models along with the queries? Is additional knowledge about existing entities also provided?\n2. Could you clarify what the KD and KA scores imply? In my view, if a model exhibits insufficient performance on knowledge differentiation, it might be due to inadequate understanding of the new knowledge or confusion brought on by existing knowledge about familiar entities. If a model performs poorly on KA, it might be because of the model's limited ability to execute multi-step reasoning or a lack of memorization of the single-hop relations within the multi-step chain.", "paper_topic_and_main_contributions": "This paper aims to assess the capabilities of LLMs on knowledge understanding, knowledge differentiation, and knowledge association over new artificial knowledge. To do this, the authors introduce KNOWGEN, a knowledge generation method designed to formulate new artificial knowledge utilizing an existing ontology. KNOWGEN leverages heredity, variation, extension and dropout of knowledge triplets to construct new entities. These new entities are then used to examine the LLMs' capabilities over new knowledge. Their results reveal that the performance of current LLMs in face of new knowledge is not satisfactory.", "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": {}}]

Author rebuttal:

Meta-review: The paper presents an interestingly clever approach for generating new knowledge connected to existing one. This is used to create a new benchmark to assess LLMs' abilities in reasoning about this knowledge and show that existing LLMs still struggle with the task by running experiments of 4 well known LLMs. Overall, all reviewers found the paper convincing and well written. Discussion was lively and constructive, and authors proved knowledgeable and willing to take into account reviewers’ suggestions for further improving their paper. The paper seems mature and timely interesting for the community. 

**Pros.**  

-  the paper presents a smart method to generate (artificial) new knowledge, which could be particularly useful for evaluating LLMs 

- it tackle an important problem, i.e. reasoning about new knowledge; 

- it shows experimentally that the task is challenging also for top LLMs; 

- it proposes a new framing of a known task;  

- it presents interesting error analyses.     

**Cons.** 

- the paper does not clearly report data about comparability of performances against existing knowledge and across models. However, details were convincingly provided during the discussion. 

- some related works seems not accounted for (see reviews)
Decision: Accept-Main