Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues
Authors: Hongru WANG
Minda Hu
Yang Deng
Rui Wang
Fei Mi
Weichao Wang
Yasheng Wang
Wai-Chung Kwan
Irwin King
Kam-Fai Wong
Venue: EMNLP 2023 Findings (2023)
Keywords: knowledge-grounded dialogue system
personalized dialogue system
large lanuage models
TL;DR: A novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating to incorporate multiple knowledge sources and address dependency issue

Abstract: Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources. To study the problem, we construct a personalized knowledge-grounded dialogue dataset Knowledge Behind Persona (KBP), which is the first to consider the dependency between persona and implicit knowledge. Experimental results on the KBP dataset demonstrate that the SAFARI framework can effectively produce persona-consistent and knowledge-enhanced responses.

Large Language Models as Source Planner for Personalized Knowledge-groundel Dialogues
Hongru Wang 9 Minda Hu Yang Deng Rui Wang?, Fei Mi Weichao Wang,, Yasheng Wang Wai-Chung Kwan Irwin King*, Kam-Fai Wong o+
Department of Systems Engineering and Engineering Management, Department of Computer Science and Engineering.
The Chinese University of Hong Kong
National University of Singapore, Huawei Noah's Ark Lab (hrwang, kfwong]@se. cuhk. edu. hk ydengênus edu. sg
Abstract
2019; Zhou et al., 2020; Wang et al., 2023c; Deng et al., 2023c), persona (Zhang et al., 2018; Liu al., 2022a; Wang et al., 2023b; Deng et al., 2023b), user memory (Xu et al., 2022c,b; Park et al., 2023; Deng et al., 2022), and more. Realizing the limitations of using single-source knowledge, some recent stud- ies further develop dialogue systems with access to multi-source knowledge (Wu et al., 2021, 2022b; Jang et al., 2022).
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing mnowledge-groundee dia- logue systems either focus single knowl- edge source or overlook the dependency be- tween multiple sources knowledge, which may result in generating inconsistent even paradoxical responses. To incorporate multi- ple knowledge sources and dependencies be- tween them, we propose SAFARI, novel framework that leverages the exceptional ca- pabilities of large language models (LLMs) in planning, understanding, and incorporating un- der both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple knowledge sources se- lection and response generation, which allows easy extension to various knowledge sources in- cluding the possibility of not using any sources. To study the problem, alized knowledge knowledge-grounded grounded dialogue dataset Knowledge Behind Persona (KBP), which is the first to consider the dependency between persona and implicit knowledge. Experimental results on the KBP dataset demonstrate that SAFARI framework can effectively produce persona-consistent and knowledge-enhancee responses.
Despite their effectiveness of enriching the dia- logue responses with multi-source knowledge, ex- isting methods typically design models to incor- porate all sources indiscriminately, resulting in a cumbersome process that struggles to handle cases dependent on the interaction between some specific sources instead of all (Wu et al., 2022b; Fu et al., 2022). Moreover, the importance of comprehend- ing the potential dependency between knowledge sources is overlooked in previous works, which may result in generating paradoxical responses (Majumder et al., 2020; Jang et al., 2022). For ex- ample, humans often express their persona with the assistance of external knowledge. As shown in Fig- ure 1(a), for responding to the question "Hi, what do you like eat?". it is inadequate only incor- porate single-source knowledge from user persona, e.g., "I am a vegetarian", since relevant informa- tion from external documents is also required, e.g. Vegetarian (Vegetarians like to eat fruits and vegeta- bles). However, being unaware of the dependency between these two different sources of knowledge (persona and documents), dialogue systems may select the document implying inconsistent personas (e.g., "Food contains meat. fruits, ..."), leading responses conflicting with defined personas (e.g., "I like to eat meat"). Therefore, attaches great importance modeling the interaction and depen- dency of different sources! in building knowledge- grounded dialogue systems.
construct a person-
1 Introduction
Knowledge enhancement techniques (Yu et al., 2022) have significantly empowered machines to deepen their understanding of the underlying knowledge in open-domain dialogues (Huang et al.. 2020), surpassing what can be solely acquired from conversational corpora. Recent years have wit- nessed various open-domain dialogue systems rely- ing on different types of knowledge sources, such as topic (Wang et al., 2022; Zhu et al., 2023), ex- ternal documents (e.g., Wikipedia) (Dinan et al.,
The absence of problem definitions and well-
"Co-Correspondigg Author.
More dependency cases can be found in the Appendix A.

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Pertena Coulg
Persowe
NULL
Anya
FARMONA
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HAME
DOCUMENTS BiRRR..R
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Docurnents
Jona repiibble.
Corsung
Frrpes Gambheret
Sec want you Gunggrhorg pronggg
()) Dependency betwees Multiple
(b) Framework
(9) An example dataset
Figure (a) An example of dependency of two sources involved persona-consistent dialogue system (PERSONA and DOCUMENTS); (b) our proposed SAFARI framework to plan, retrieve, and incorporate multiple sources of knowledge: PERSONA, DOCUMENTS, and so on. Planning, Retrieval and Assembling steps are divided by dashed lines: (c) A sample from the KBP dataset. There are three situations of responses our datasets: 1) response without the need for any sources (NULL), 2) response using only personae description (from PERSONA source), and 3) response using both persona and knowledge (from PERSONA, DOCUMENTS sources). The example here presents the first and third situations. We highlight the response and used knowledge with the same color.
established benchmarks greatly impedes the progress of building dialogue systems that can capture the knowledge dependency between dif- ferent sources. To this end, we construct a per- sonalized knowledge-groundee dialogue dataset, named Knowledge Behind Persona (KBP), to mimic the scenario where the understanding of persona-knowlede dependency is required to pro- duce consistent responses. KBP aims to build better persona-consistent dialogue systems with the help of utilizing underlying knowledge behind different persona descriptions comprehensively.
Additionally, our framework easily accommo- date scenarios where multiple sources are required. To sum up, our contributions are listed below:
We propose the SAFARI framework to augment the dialogue system to plan and incorporate mul- tiple sources of knowledge into responses ((e.g.. decide whether or not require knowledge, which source to call, and when to call)), and further ad- dress the knowledge dependency issue between sources in both supervised and unsupervised manners by leveraging LLMs.
In order to address the interaction and depen- dency issue between specific sources, we pro- pose a novel framework, named Source plAnner For personAlized knowledge- gRounded dlalogues (SAFARI). Seeing the potential of large language models (LLMs) in planning the use of external in- formation (Schick et al., 2023; Shen et al., 2023; Gao et al., 2023), we explore LLMs' capability of connecting different sources in the context of the personalized knowledge-grounder dialogue system. As illustrated in Figure 1(b), the whole response generation can be modeled into three steps in SA- FARI: 1) Planning to make a series of decisions of whether to use a specific knowledge source given relationship descriptions between different sources; 2) Retrieval to retrieve top-n results from external databases according to the decisions; 3) Assem- bling incorporate all retrieved knowledge into the final response generation. Benefiting from de- coupling source selection and response generation, our framework is more flexible and scalable, allow- ing independent modification of each component.
We build personalized knowledge-grounded di- alogue dataset, KBP, where the responses are conditioned on multiple sources of knowledge, leading to more user-engaged dialogues with in- formative and persona-consistenl knowledge.
We conduct exhaustive experiments to validate the effectiveness of our proposed framework to incorporate multiple sources and capture the de- pendency between them?.
2 Related Works
2.1 Personalized Dialogue System
To build personalized dialog agent, Zhang et al. (2018) extensively investigated this task with a new dataset Persona-Chat, where a pre-defined per- sona set is form of multiple sentences of tex- tual description. Lots of works follow this setting and have taken mutual persona perception (Liu
'The code can be found in ruleGreennSAFART/

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al., 2020; Kim et al., 2020; Xu et al., 2022a), persona-sparse scenario (Song al., 2021; Welch et al., 2022), long-term persona memory (Xu et al., 2022c), persona extending (Liu et al., 2022b) and persona order bias (Chen et al., 2023) into con- sideration. Although some of them complement the insufficient semanties in short persona descrip- tions by further utilizzig external commonsense knowledge base to extend existing persona sets (Majumder al., 2020; Liu al., 2022b), they still fall into the conventional framework coupling the knowledge selection with the response gener- ation (Wu et al., 2022b), rendering it infeasible to handle various sources of knowledge. There have also been works showing that the combination of different knowledge sources such persona de- scriptions and Wikipedia can further improve the overall performance (Jang et al., 2022; Wu al.. 2021, 2022a). However, they still fail to capture possible dependency between knowledge sources, In their framework, knowledge is not used as the role to assist persona-consistent response genera- tion, but as an additional resource to generate a more informative response (Dinan et al., 2019; Xue et al., 2023) or select suitable persona (Jang et al., 2022; Fu et al., 2022).
model the recissonnmmakngg process of the call or- der for multiple knowledge sources (Wang et al., 2023a). We differ in exploring the planning capa- bility of LLMs to decide whether or not require knowledge, which source to call, and when to call in both the supervised and unsupervised manner.
3 Data Collection
In this section, we detailedly introduce the process of data collection and statistics of the collected data. The data collection process can be divided into two steps: Step 1. Persona and Knowledge Acquisition and Step 2. Dialog Collection.
3.1 Persona and Knowledge Acquisition
Seeds Preparation. To reduce annotation cost, we take advantuge of the currently available persona dialogue dataset: DuLemon (Xu et al., 2022c) and two widely-adopted Chinese knowl- edge bases: Baike² and Ownthink to produce seed data. Specifically, we first cluster all persona sen- tences from DuLeMMnn into 10 topics. After re- moving duplicate, rare, and similar personas, we carefully choose around 20 personas for each left topic seed personas In addition, we manually add some personas for the existing topics and new topies. The final personas consist of age, nation, personality, career, movie, music, sport, book, con- stellation, locality, gender, others. For retrieving persona-related knowledge, we simply combinc two aforementioned knowledge bases with similar filtering operations and store them as (head entity, attribute, tail entity) tuples.
2.2 LLMs for Planning
Large Language Models (LLMs) show remarkable capabilities in planning the use of various exter- nal resources, such as tools (Schick et al., 2023), models (Shen et al., 2023), and APIs (Li et al., 2023), to solve various NLP tasks and suit differ- ent applications in practice. Alternatively, different types of knowledge can be retrieved from external sources, as illustrated in WebGPT (Nakano et al., 2022) and ReAct (Yao et al., 2023). Integrating various knowledge sources improve the quality of LLM generation becomes increasingly challeng- ing due to the need for strategic planning, sequen- tial decision-making, and complex reasoning. Pre- vious research primarily focuses on either earlier decision-making stages (Nakano et al., 2022; Shen et al., 2023) or the subsequent response generation (Sun et al., 2023; Schick et al., 2023; Deng et al., 2023a), instead of establishing a complete frame- work for planning the use of multiple knowledge sources to generate appropriate responses. There is a latest work named TPE which regards different knowledge sources as conceptual tools and pro- poses a multi- collaboration framework to
Persona and Knowledge Matching. For each persona sentence, we segment it into a sequence of words with a Chinese word segmentation tool jieba". any words exactly match the head en- tity or tail entity of a certain knowledge tuple, we transform the tuple into a sentence according to pre- defined templates and then save it as one knowl- edge for this persona sentence. In this way, we can obtain various knowledge for each persona sen- tence. Consistent with previous works (Zhang al., 2018; Majumder et al., 2020), we randomly sample persona sentences along with knowledge sen- tences per persona to form a persona description
"http://www.op""" wwt.:..ppnggccommm
"http: //gi wttphh......w.tttt.tteteo
'Some topics are removed they contain less than personas. We don't I pick hundreds of personas because one persona sentence has a vast amount of knowledge behind htttps: / thub. com/fxsjy (xsjyyjjiell jjbba

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of the system for each dialog. More details and templates can be found in Appendix B.
KBP
Train
Valid
Test
# dialogues
1,981
248
248
# samples
9,821
1.227
1,229
3.2 Dialogue Collection
# avg turns
4.96
4.93
4.96
# utterances
19,642
2,454
2,458
Collection Setting. Following the setting of Jang et al. (2022), annotators are instructed to make a dialogue by considering persona and correspond- ing knowledge under the single-person setup. In this way, one person can better understand what persona to ask as the human and what knowledge to use the system, in comparison with two in- dependent persons playing two roles separately. During the collection, each annotator first selects a suitable persona and then optionally identifies relevant knowledge, giving a knowledge-cnhanccd and persona-consistent response at last.
# avg length
17.6
17.3
17.5
# resp w/ persona
86.1%
84.4%
85.3%
# resp w/ p_ and k
76.3%
74.2%
75.1%
Table Statistics KBP dataset.
erating a response may rely on persona alone both persona and knowledge. With the labels of these grounded sources, the planning abilities of the dialogue systems be quantitatively measured.
3.3 Statistical Analysis
We organize the collected personas (PERSONA source), persona-related knowledge (DOCUMENTS source), and dialogues the form shown Fig- ure I(c). We finally collect 2,477 dialogues and 24,554 utterances with turns per dialogue on av- erage. We then split the collected data into train, validation, and test sets using the 8:1:1 ratio. The dataset statisties ing the number of dialogues, utterances, average length, as well as data sources used. The aver- age length per utterance reaches 17.6, hinting at the informativity and depth of the conversation. It is shown that over 86% of responses used per- sona (i.e., resp w/ persona) and 76% used both per- and knowledge (iee,, resp w/ p_ and and_k), which shows that KBP is capable benchmark to eval- uate the different grounding abilities of models.
Training and Pilot Annotation. All annotators are first required to take training tutorial to learn the annotation procedure, requirements, and exam- ples of annotated dialogues. Afterward, they are given 30 personas make 10 dialogues. We pro- vide corresponding feedback to help them adjust their annotation criteria. To establish the necessity of persona-knowledge dependency, we consider the situation where the response will be persona- inconsistent without the assistance of knowledge. To this end, annotators are requested to ask ques- tions centered on the implications based on knowl- edge and persona. For example, the annotator is supposed to ask "Which province are you from?" instead of "Which province does Shenzhen belong to?", given the persona live in Shenzhen" " and corresponding knowledge "Shenzhen belongs to Guangdong province".
summarized in Table 1, includ-
Method
Batch Collection. After pilot annotation, conduct dialogue collection batch by batch and reg- ularly coach the quality of collected data?. For each batch, we sample personas different from pre- viously annotated dialogues to increase its diversity in the whole dataset. The addition, deletion, and re- vision of persona and knowledge are also accepted and updated at the batch level
4.1 Task Definition
We first provide a general definition of a dia- logue system that requires multiple sources and then instantiate the definition in the text of personalized knowledge-grounded dia- logue. For each dialogue, the dialogue context = (wi, 81, U2, 82, 02,...,u1 u1) and different knowl- edge sources K = {K1, K2..... K.} are provided, where K; = [k] k2 p..., "..,....]] {.j indicates the ith source's name of K. K/ denotes the jth knowl- edge in natural language from K; If K₂ is reliant on Ki, knowledge should be retrieved from K2 based on the selected knowledge K₁. Such re- liance should also be embodied in the construction
Grounding Annotation. We also gather the labels grounding knowledge sources for the system's responses by asking the annotators to specify the sources they draw from while providing responses, such as PERSONA or DOCUMENTS. For instance, gen-
We write python script to check typos (e.g. labels used knowledge is not exist given knowledge bases) provided feedback after each batch.
"The annotators must check for persona conflicts and re- frain from relying too much on single knowledge.
"The knowledge related to the same persona forms a docu- ment, and different documents form DOCUMENTS source.

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of K2, in a way such as K2 {M} [kg
kg),
Step 2: Retrioval
PARSONA POLUMENTS
{ke kg)..... The goal of the system is gen- erate a response Sn conditioned on с and a set of knowledge [k,,,...AA3) retrieved from K if re- quired Specifically in the context of person- alized wowledge--gronnder dialogue, we regard (PERSONA, DOCUMENTS) as (K1, K2) respectively, There is potential dependency between these two sources, and the goal is generate response 8t conditioned on set of knowledge {p].....:] kmh J. where pi is retrieved from PERSONA and km is re- trieved from DOCUMENTS. The response can also be generated conditioned on a set of knowledge from single source PERSONA without any sources.
Retriever
Soorces:
Retrioved Resalts
System Reqpasze
Large Language Models (LLMs)
Dising Hisury
PESSONA DOCUMENTTS
Retrived Reseits
Step 3: Planning
Step 3: Assembling
Figure 2: The supervised framework of SAFARI for personalized knowledge-groundd dialogues. We use different colors to indicate different steps. The black arrow denotes the flow of data without the the involve- ment L.M.
4.2 Supervised SAFARI
and the agent can skip this step; (2) the response needs multiple sources of knowledge, and the agent strictly follows the output source order to retrieve top-n related knowledge k for the ieh knowledge source according the dialogue context c, and if there is dependency here, will use preced- ing retrieved results k in the planned execution order as a filter. Specifically, assuming the output order PERSONA, DOCUMENTS in the planning step for a persona-consistent dialogue system, we first retrieve top-/ result p" from PERSONA, and then we retrieve k- from DOCUMENTS according to and p". Here the utilization of p* depends on the system- atic design. For example, if there is designated source K for each p" (a.k.a dependency). we can simply retrieve k from K*. And there is another case where all knowledge stored together and we can concatenate e and p" as a query in the retriever.
There three different steps in our proposed SA- FART framework: Planning. Retrieval, and As- sembling as shown Figure I(b). We will intro- duce them step-by-stey under both supervised and unsupervised settings.
Planning The goal of the planning step is to make series of decisions to decide whether or not the corresponding source of knowledge required and determine their call order if needed. Since the dependency relationship is previously known, we only need to make sure that a certain knowl- edge source is called after the sources it depends on. Thus, we formulate this task as sequence-to- sequence generation by directly outputting either required sources in execution order NULL if the response does not need any knowledge as follows:
M : c - K2, Kj....,K Kn or NULL, (1)
R: K1, Kj...., K,.,,,,,,,,,,,, + ke...., p.
where M is parameterized by LLMs. We add K,,... K,, and NULL into the vocabulary of LLMs special tokens. The key idea here is similar to the recent ToolkenGPT (Hao et al., 2023), which regards different tools as special tokens the vo- cabulary. Besides that, we add other special to- kens to indicate the different parts of the input, i.e., [SOURCE] and [EOS] to indicate the start and end positions of sources. In this way, LLM can model the dependency between different sources and learn when and how call certain sources.
We concatenate all preceding results together with the dialogue context to generate the response:
Assembling
M: : Imp 81,
(3)
where Inp = (c [SOURCE] K₁, [EOS] [MIDDLE]M, A [EOM]]. [MIDDLE] and [EOM] represent the start and end positions of retrieved results. Forming the input in this way has two ad- vantages. Firstly, the name of the sources indicates the type of results retrieved, which provides more signals to the LLMs. Secondly, it allows to train the language model in multi-task manner using teacher forcing. The loss is only calculated on to- kens related to the planning and the response as shown in Figure 2. We first predict the planning
Retrieval According to the output of the last step, there are two cases in this step: (1) the response does not need any external sources of knowledge,
10 Unlike most of the previous works, we also consider the response that does not need any knowledge in our setting.

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and then generate the response according the preceding results when inference.
demonstrations from the train set the zero-shot prompts during evaluation.
4.3 Unsupervised SAFARI
5 Experiments
Inspired by the recent progress using LLMs as a controller to plan a call order of different mod- els (Shen et al., 2023), we adopt a similar way here by providing detailed prompts to leverage the LLMs' capability to address the dependency be- tween different knowledge sources. We consider two settings: zero-shot and in-context learning for planning and assembling steps here since the re- trieval step the same as above.
5.1 Experimental Setups
Implementation Details. We mainly choose BELLE-LLAMA-7B-22 (Ji al., 2023) and ChatGLM-6B (Du et al., 2022; Zeng et al., 2023) as two backbone models for supervised setting since they are two popular open-source Chinese models. And we additionally add ChatGPT (gpt- 3.5-turbo-0301)" for the unsupervised setting. For training, we set the batch size as train models with epochs and save the checkpoint with the lowest validation loss. For other pyper-parameed settings, we mainly follow the corresponding official code Due to the computation limit, we conduct training with LoRA (Hu et al., 2021) at one single 3090 GPU, and it cost about 4-6 hours. For the unsupervised setting, we set both the temperature and top as 0.1 to reduce the randomness of LLMs.. Besides that, we use three types of retrievers including both sparse and dense retrieval: BM25 (Robertson and Zaragoza, 2009), DPR (Karpukhin et al., 2020), and RocketQAv2 (Ren et al., 2021). We only retrieve the top-ranked result from each source in the experiments
There are different knowledge bases storing relevant infor-
mation:
K 1: [K] DESC)
K 2: [K 2 2_ESSC
.....
There exists dependency between these knowledge hases. (DEPENDENCY _DC_CC
Here the dialogue between the user and the system: (DI- ALOGUE)
Based the user's last question, please determine if it requires invoking the corresponding knowledge base. If the invocation necessary, output the names of the knowledge bases in the order they should he invoked. invocation necded, output NULL.
Table 2: The zero-shot prompt of unsupervised SAFARI at plamning step (translated from Chinese to English).
Evaluation Metrics. During the evaluation, we use different metrics at different steps. We use Fl for planning, Recall @ 1 for retrieval, and BLEU (Papineni al., 2002), Rouge-L (Lin, 2004). For assembling. Knowledge Consistency (K.C) and Per- sona Consistency (P.C) are calculated using our finetuned NLI models (Madotto et al., 2019). More details including retrieval models, NLI models, and NLI metrics can be found in Appendix C.
The dialogue is as follows: (DIALOGUE)
The following knowledge retrieved from different sources of knowledge bases:
[MIDDLEE (EL_E_RRSUTTSS
Please play the role of the system and generate a reply ac- cording to the context of the dialogue and given knowledge. Please make sure your reply is consistent with the given knowledge. If the provided knowledge is NULL, generate response solely based on the dialogue context. System:
Table 3: The zero-shot prompt of unsupervised SAFARI at ussembling step (tramslated from Chinese to English).
5.2 Performance of Planning
There are three types of decisions representing different sources required in the next step: NULL, PERSONA, and Both (selecting both PERSONA and DOCUMENTS). Table 4 demonstrates the Fl of plan- ning under different settings. Under supervised settings, despite LLMs achieving high Fl scores at Both, the performance at NULL and Persona is still unsatisfactory, since there are fewer training sam- ples in these two cases. On the other hand, under
Planning. Instead of directly providing supervision signals, we provide a description for each source knowledge, accompanied by the corresponding dependency between the sources. The prompts are shown in Table 2.
Assembling. We feed the dialogue content and the retrieved knowledge into the prompt as organized in Table 3, adapting LLMs generate responses according to dialogue context and retrieved results. The full prompts of the unsupervised planning step can be found in Appendix D. For few-shot in- context learning, we prepend three corresponding
"https://openai. http:///enaaa..oom//o////////eee com/blooo/chhtgtt /https://githhn Comm/THDD//ChtaL.N and https:
//ithub. . wwwmmmaajiaaecc/BBBE..
l'e effects of different choices is analyzed at Section 6.

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Model
NULL
Persona
Both
Both
Model
Persona
Supervised
PERSONA
DOCUMENTS
DOCUMENTS
BELLE-LLAMA 2M
42.67 (194)
14.08 (17)
83.77 (1018)
BM25
36,80
48,97
15.05
11.37
CHATGLM-6U
47.10 (129)
31.96 (69)
86.59 (1031)
RocketQAv2
80.00
92.31
50.49
35.75
Unsupervized
DPR
83.20
93.07
51.67
39.33
Zero-alor
BELLE LLAMA- 7B-2M
2835 (940)
8,94
(2347
Table 5: The performance of Retrieval of different types of retrievers. There are 125 examples that only require PERSONA and 923 require both PERSONA and KNOWLEDGE. EDGE. We also report the @ecall@) of DOCUMENTS without dependency (DOCUMENTS*).
CHATGLM-6B
25.60 (1225)
0.0 (0)
0.43 (4)
CHATGPT
11.45 (1 16)
20.67 (233)
74.88 (880)
Inconteer
LOLLL-LLAMA-7--M
9.22 (36)
(112)
7.0
CHATGLM-68
25.67 (1190)
1.49
4.62
CHATGPT
27.95 (699)
23.14 (238)
41.98 (292)
Table 4: The F1 of different decisions in Planning of different I.LMs under supervised/unsuperviedd settings. We also report the frequency of different decisions in the bracket. There are 181 NULL, 125 PERSONA and 923 PERSONA, and DOCUMENTS in the ground planning.
DPR the retriever in our periments afterward.
Therefore,
5.4 Performance of Assembling
Table demonstrates the performance of response generation under both supervised and unsupervised settings. Referring to Table 4, the performance of the planning step largely affects the results in the assembling step, when the retriever is the same. Mostly, better planning leads better responses in all metrics. The supervised models are much better than unsupervised models since their planning re- sults are much better, while ChatGPT performs best under unsupervised settings due to a similar rea- son. We found that BELLE achieves higher BLEU and Rouge-L, K.C but lower P.C than ChatGLM since the planning gap between them mainly comes from PERSONA. In addition, due to poor retrieval performance at DOCUMENTS (Table 5), the consis- tency score K.C also much lower than P.C. With demonstrations in the prompt, we observe generally better performance on most metrics, since LLMs tend to accept the personalized role, rather than gen- erating responses like "As an Al language model, I do not have persona ....". " Overall, we conclude that the grounding ability of supervised models is much better than unsupervised ones, and ChatGPT performs best under the unsupervised setting.
unsupervised settings, the LLMs are over-confident their decisions to use NULL, and they misunder- stand the dependency between different sources (sometimes deciding to only use DOCUMENTS with- out PERSONA) This result reveals the LLMs' low accuracy in expressing uncertainty and fetching un- known knowledge. Furthermore, in-context learn- ing cannot improve this situation, which is similar the observation in Amayuelas et al. (2023).
5.3 Performance of Retrieval
With the ground-truth planning labels (except NULL), we examine three types of retrievers, includ- ing BM25, RocketQAv2, and DPR, to evaluate the retrieval performance. Table 5 presents the Re- call@1 1 (R @1 1) of the different retrievers. We found that the DPR and RocketQAv2 can achieve over 80% @1 when retrieving from PERSONA source while only about 50% from DOCUMENTS and the R@I 1 at DOCUMENTS! further decreases after remov- ing the dependency. First, the semantics between different knowledge from DOCUMENTS with the de- pendency are similar to the same underlying per- sona p", making them more difficult to be distin- guished. In addition, noisy knowledge sentences are introduced since there exists dependency. Moreover, we observe that DPR performs the best out of these three retrievers all sources of knowl- edge while BM25 performs W5 revealing the importance of dense retrieval models in this task.
Analysis
In this section, we analyze the effects of differ- ent components and the choice of the number of retrieved results, based on ChatGLM under the su- pervised setting. In addition, we conduct human evaluations to verify the quality of automatic eval- uations.
We assign the case that LLMs predict DOCUMENTS only as NULL since this case does not exist in KBP.
6.1 Impacts of Different Steps
"RocketQAv2 is generally not competitive with DPR be- cause of the pre-trained weights in the RocketQAv2, since it is pre-trained using QA datasets and the length of the question is much shorter than dialogue context.
We investigate the effects of individual steps by providing the model ground-truth labels from each step to generate the response, enabling us to an-

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Model
BLEU1
Rouge-L
P.C
K.C
Model
BLEUI
Rougel.
P.C
K.C
Supervised Setting
CHATGLM-66
23.81
26.70
76.99
42.39
BELLE-LLAMA- 7B-2M
30.48
34.61
75.34
46.62
Ground Planning
24.29
27.01
86.16
57.12
CHATGLM-68
23.81
26.70
76.99
42.39
+ Ground Retrieval
25.86
29.15
79.52
53.95
Unsupervised Setting
Ground P & R
25.71
29.43
90.56
72.99
Dependency
23.32
25.53
75.67
38.49
Zero-shot
HALLE-LEAMA--E-22
Tf.84"
19.24
30.59
27.37"
Documents
23.06
25.34
75.91
36.53
CHATGLM-68
6.18
14.50
14.73
24.73
Planning"
23.51
25,98
72.90
24.89
CHATGPT
12.06
24.44
73.47
38.00
Planning"
23.69
26.81
71.60
34.91
In-content
BELLE-LLAMA-
19.51
22.25
72.98
24.89
Table 7: Ablation study on the impact of different steps and modules in SAFARI.
CHATGLM-66
13.74
19.69
16.92
24.89
16.03
25.62
35.56
Assembling
Table 6: The performance of Assembling under super- vised/unsupervised settings.
Number
BLEU1
RougeL
P.C
K.C
23.81
26.70
76.99
42.39
22.70
25.57
71.03
29.45
alyze and understand the specific effects of each step in a clear and systematic way. Table 7 presents the results. First, we note that the inclusion of ground-truth planning labels or knowledge casts a positive impact on performance. Planning pri- marily enhances P.C and K.C, while grounding knowledge contributes to BLEUI and Rouge-L. scores. The best results are obtained when both signals are combined. Secondly, we also conduct an ablation study by removing some modules: 1) removing dependency information (-Dependency); 2) removing DOCUMENTS and only using PERSONA (- Documents); and 3) removing the planning step by always selecting PERSONA (-Planning") or always selecting PERSONA and DOCUMENTS (-Planning*) for each turn. It be found the bottom of Ta-- ble 7 that all metrics are dropped differently after removing different components except for Rouge- L when always selecting two knowledge sources. To conclude, SAFART can effectively incorporate multiple sources (compared with -Documents) and further address dependency issues (compared with Dependency). Moreover, SAFARI demonstrates its versatility by effectively handling multiple sources and efficiently selecting relevant ground knowl- edge. Notably, SAFARI outperforms existing methods that indiscriminately utilize all available sources (compared with *lannning**.
20.69
24.05
69.73
27.91
Table 8:
The performance of Assembling of different number of retrieved results.
performance of response generation decreases with the number, which indicates that noisy knowledge will harm the quality of the generated responses.
6.3 Human Evaluation
Human evaluation is conducted to evaluate the qual- ity of generated response in terms of three met- ries: coherence score (Coh.), persona consistency score (Per.Cs), and knowledge consistency score (Know.Cs). We randomly sample 100 responses with grounding information for each model and ask three annotators to indicate its coherence score (1-5) and whether the response consistent with the given persona (1/0), and knowledge (1/0). Ta- ble 9 shows the result. We observe that supervised methods achieve higher performance than unsu- pervised ones, which corroborates the findings of the automatic evaluation results presented in Ta- ble 6. Besides that, we found BELLE achieves the highest performance across all metrics and outper- forms ChatGLM since the effects of planning are not considered during human evaluation. More- over, we also found that in-context learning brings lower rejection rate and more human-like re- sponses. Specifically, the rejection rate of BELLE under the setting of zero-shot learning is about 32%, while the number is reduced to 1 12% under in-context learning
6.2 Different Numbers of Retrieved Results
The number of retrieved results plays key role in the response generation. There is trade-off between accuracy and recall, while small number of retrieved results may not cover enough seman- ties but a large number may introduce additional noises. Table 8 presents the results of the different numbers of retrieved results. We observe that the
Conclusion
In this paper, we propose novel framework SA- FARI to incorporate multiple sources of knowledge
More analysis can be found in Appendix

---

Model
Coh.
PenC's (%)
Know,Cs (%)
safe and harmless. We note that LLMs may in- herit hallucination issues shown in the planning analysis, and will plan not to use corresponding sources due to poor performance to express uncer- tainty. The calls of the OpenAT API this paper were carried out by Dr. Yang Deng, one of the cor- responding authors from the National University of Singapore.
Supervised Seiting
B 2
4.38
72.0
63.8
CHATGLM-6B
4,06
68.0
39.1
Unsupervised Setting
Zero-thot
BELLB-LLAMA--ER
7B-M
2.84
24.7
19.5
CHATGLM-66
2.58
17.0
14.8
CHATGPT
4.00
63.4
33.3
In-context
BELLB-LLAMA-70-22
3,36
40.0
21.7
CHATGLM-6R
2,88
32.0
28.9
The human inspection and annotation were conducted by a reputable data an- notation company, and the annotators are compen- sated fairly based on the market price without re- vealing any personal information. Besides that, our dataset may contain biased opinions due to the subjectivity of manual annotation.
Human Annotation
CHATGPT
4.03
54.0
48.8
Table 9: The results of human evaluation. The inter- agreement about 86%.
bases and further address the dependency issue be- tween them. Unlike previous works, SAFARI can be extended to multiple sources easily and it can handle cases that do not require any sources or re- quire some instead of all sources between them. We build the first personalized showledge-grounddg dialogue (KBP) dataset, and experimental results prove the effectiveness and robustness of SAFARI,
Acknowledgement
We would like to express our heartfelt gratitude to all anonymous reviewers for their insightful comments and suggestions. The work described here was partially supported by grants from the RGC General Research Funding Scheme (GRF) 14222922 (CUHK 2151185).
Limitations
In this paper, we propose the SAFARI framework to incorporate multiple sources and address the de- pendency issue between them by leveraging the exceptional capability of LLMs. However, we knowledge two major limitations of the paper:
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A Dialogues Requiring Knowledge Dependency
Attributes mick rame, birll_ppace.... rewardk, members.
Templates
[bcad emityl's I's [attribucc is jail enity)
The [utnibutt] [head entry) contains (tail entity)
Table 10 shows another example of knowledge de- pendency. In this example, without considering the persona-knowledge reliance, the entity (proverb A),, which is irrelevant to the band * f JE3,, is retrieved. Although the retrieved knowl- edge is unrelated to the ground persona, ChatGPT still tries combine these two disconnected pieces of information and generate an inconsistent re- sponse (Response w/o. Dependency).
Table II; Two major templates to convert triples into natural language descriptions (translated from Chinese to English).
And then we filter duplicated words to reduce un- necessary computation. For the left words, we map them to the corresponding knowledge base by one. We discarded some useless attributes such penmanship and pronunciation, and then translate the matched triples natural language sentences according to pre-defined templates as shown Table 11. If we do not find any matched triples, we just discard the persona. At last, we manually checked each persona description to make sure (1) there is no repetitive knowledge statement, and (2) each persona contains least 5 knowledge state- ments
Ground Persona: "I really like H.t K DE (No Party for Cao Dong)"
Retrieved Knowledge w/o. dependency: "AA (The
Clod) is a Chinese proverb that metaphorically represents a motley crowd."
Ground Knowledge w. dependency: "AL 4t is a new album from the band E (
User: Do you want to listen to & (The Clodj? Response w/o. Dependency: I absolutely love # a proverb that descrihes motley
C Implementation Details
1 Mi:! FL crowd, representing group of people without organiza- tion coordination. So regarding At, it represents a group of individuals lacking organization coordination. Do you have any other questions I can assist you with?
We first illustrate the details of finetuning and then introduce our definition of NLI metrics: P.C and K.C.
say that
Retrieval Models. We finetune RocketQAv2 and DPR using our own KBP dataset by regarding (context, used_persone / document) as the positive and (context, wereoated_persona / document) as the negative. We set epochs 5 and max sequence length 512, and mainly follow the scripts: https://ggt http::/ggttth.h..ee httpttt..o//a/deeeeaaaeeoeooeeea t and
Response Dependency: You bet! I absolutely love # 4 MRRj! 5 their newly released album, and I'm really looking forward listening the songs Would you like to share with which song you enjoy the most in A.
Table 10: An example dialogue with ChatGPT showing persona-knowledge dependency (translated from Chi- nese English)
https:///github.ccm///ll cot.///ba.cmmmllbbbbbbba.... Mullii-CRR/trree/mannnrttttiii for other parameters. For RocketQAv2, we load the weights of pre-trained model zh_ dureader _d__2 as introduced in the official homepage, which is trained on the largest Chinese QA dataset, and use 12-layer porr-baase-hinnes with 110M parameters backbone model for DPR. NLI Models. Following previous work (Kim et al., 2020; Cao et al., 2022), we finetune an NLI model (Welleck et al., 2019) using our own dataset by regarding (ground_persone / document, response) as the positive and randomly sampled (unrelated_ persona / document, response) as the the
respectively
B Data Preparation
Seed Preparation In the original persona dataset, DuLeMon, most of the persona does contain much knowledge. For example, about 30% 40% of persona is about the name such as "my name is XXX". After clustering, we removed topics that contain less than 20 personas. There are topics left: career, book, music, movie, personality. Then we add some basic persona such as gender, age, locality, nation, sport, and others.
negative. We also use bert bert-base-chiness backbone model. We concatenate and encode the ground persona/document & and response in the form of [CI.S][(EP))>SEPP) and we train the
Persona and Knowledge Matching For each per- sona description after segmentation, we first re- move stop-words!? and frequently used words.
17 master/ccn_stoworrss.u txt
We will manually add it if there are less than 5 knowledge statements.

---

NLI
if there is grounding g for r, and planning also uses g'- if there is grounding for and planning does not use g'. if there is no grounding 9 for and planning uses g'. if there is no grounding 9 for and planning does not use g'.
0,
C(r.g)
(4)
0,
1,
model predict whether responses are consistent with corresponding personas or documents. The batch size for fine-tuning 8. The maximal train- ing epoch is 5, and the max sequence length of the encoder is 512. In the experiments, we use the AdamW optimizer with a learning rate of 2c- 5 and an epsilon of le-6. We evaluate the NLI model the KBP test set every 500 iterations dur- ing training, and we save the checkpoint with the highest performance on the KBP test set. The fine- tuned model achieves > 95% accuracy for both persona (95.94%) and knowledge (96.95%) on the KBP test dataset. We then calculate the persona consistency (P.C) and knowledge consistency (K.C) according to the output of the NLI model. Com- pared with many previous works (Madotto al., 2019; Cao al., 2022) that calculate the NLI score only when ground-truth knowledge includes per- sonas or documents, our framework more sophis- ticated and introduces the planning step. which con- siders the situation where responses do not require any ground knowledge. Thus, if we only calculate the consistency score in the occasion where there exists ground truth personas/documents,, it is un- fair and inaccurate for our framework, since wrong ground knowledge could also hurt the quality of responses and the system is not penalized when gives wrong planning (e.g. always calling external sources). We design a new calibrated metric for dialogue consistency as described in Eq 4.5,6,7:
decides not to use g': 3). there is no grounding g for r, while the planning decides to use g'; 4). there exists no g, and the planning step decides not to use g' either. We only calculate the NLI score (g, r) using finetuned NLI model in the first case of Eq 4. With this defimition, we can get the calibrated score P.C and K.C for the KBP test set.
D Prompts Templates under Unsupervised Setting
Table 12 demonstrates the zero-shot prompt of the SAFART planning step on the KBP dataset, and the zero-shot prompt for the assembling step is the same as Table 3.
There are two knowledge bases storing relevant informa-
tion:
This knowledge hase stores information re- lated to system personas, such as gender, age, place of origin, hobbies, personality traits, and other relevant data. This knowledge base stores domain- specific knowledge related to system personas, such as the domain knowledge about the place of origin.
PERSONA:
DOCUMENTS:
There exists dependency between these knowledge bases. The invocation of DOCUMENTS relies on the results from PERSONA. Please ensure the correct order of invoking them.
Here is the dialogue hetween the user and the system: (di- alogue history] alogue_history) Based on the user's last question, please determine if it requires invoking the corresponding knowledge base. If the invocation is necessary, output the names of the knowledge bases in the order they should be invoked. If no invocation is needed, output NULL.
Table 12:
The prompts of unsupervised SAFARI on KBP dataset (translated from Chinese to English)
{ 1, if entails g 0, if r does not entail g
NLI(r,
(5)
E Human Evaluation
P.C Σn C(ripp)
(6)
We find that human more likely to find persona- inconsistent cases. There are some responses that have intra-sentence contradictions (Zheng et al., 2022), for example, "My zodiac signs are Aries and Taurus". In addition, there are other responses related to the persona description that are incon- sistent. Both these types of responses are easy identify by humans but hard for the NLI model to detect, resulting in lower Per during human evaluation.
K.C End C(rikk)) m
There are four cases during experiments as shown in Eq 4: 1). there exists ground-truth ground- ing g for the response in the test and the plan- ning also decides to retrieve ground knowledge g' from corresponding sources (cither PERSONA or DOCUMENTS); 2). there is for r, but the planning


Reviews:
[{"review_id": "bfhp1yiJmw", "reviewer": "Reviewer_qWAp", "summary": "", "questions": "A: Could you please clarify the meaning of lines 302-307 in Section 4.1? How does this particular organization of information influence the follow-up method?\n\nB: The framework shown in Figure 1(b) incorporates multi-source knowledge, including persona config, Wikipedia, and memory. However, in the experiments, only the persona config (persona description and reliant knowledge) was utilized. Have you evaluated the framework using real-world applications, such as incorporating other general knowledge sources?\n\nC: Since the persona config is retrieved, how do you ensure persona consistency? Could it be possible to set the persona config (description and reliant knowledge) as fixed to achieve consistent personalized dialogues?\n\nD: Have you evaluated other knowledge-grounded dialogue generation models using the KBP dataset as baselines for comparison?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback and suggestions. For your raised concerns, we will address them one by one as shown in the following:\n\n**Reasons to Reject:**\n\n1. We appreciate the comment and we also acknowledge the great number of sources is valuable as discussed in Limitation. However, we would like to clarify that this work focuses more on **the planning capability of LLMs to decide whether or not require knowledge, which source to call, and when to call.** Specifically, we target the interaction and dependency issues between different sources of multi-source knowledge-grounded dialogue generation. Distinguishing from previous works that incorporate all sources indiscriminately (resulting in cumbersome processes and unnecessary computing cost), we explore three different situations: response without requiring knowledge; response requires one source of knowledge; and response requires two dependent resources of knowledge. \n\n2. Our dataset KBP is constructed targeting personalized response generation, which is **one of the key research problems in dialogue tasks**. We utilize knowledge related to persona to investigate dependency issues across different sources of knowledge. As shown in Figure 1(a) and Appendix A, without the assistance of related knowledge, it is relatively easy to generate persona-inconsistent responses. \n\n3. We provide both supervised methods and unsupervised methods to investigate the performance of LLMs as source planners. For the supervised methods, the LLMs are trained to learn the planning capability and grounding capability simultaneously. Since the planning step is based on the dialogue context, it is agnostic to the knowledge used in the dataset. We also want to note that there is no other suitable dataset (e.g., contains responses that do not use knowledge and use multiple sources of knowledge simultaneously; and the dependency between different sources of knowledge), and the persona and documents in the test set of our proposed KBP are unseen during the training.\n\n\n4. We have considered some baselines from previous works such as FoCus [1] and MSKE-Dialog [2] which target multi-source knowledge-grounded dialogue generations. However, we found that almost all previous works assumed that the response must require external knowledge and incorporate all sources indiscriminately. For example, FoCus assumed that each response must require knowledge. We focus on more complex and frequent situations in practice where the response may need or not need the involvement of different sources of knowledge. Furthermore, we also model the dependency relationship between different sources. Despite handling different problem settings, we further conduct experiments with these methods on the concerned problem below (Please refer to Question D).\n\n**Questions for The Authors:**\n\nA: Lines 302-307 introduce the dependency issues across two different sources of knowledge. In our paper, the DOCUMENTS knowledge is reliant on PERSONA knowledge, as shown in Figure 1(b), There are many pieces of knowledge (for example, commonsense knowledge) behind one persona description. For simplicity, we need to open the Music App to find the latest songs. Here we use \"I love to listen to pop music\" as a persona description, and some names of pop songs as knowledge such as \"current pop songs include xxx\". When the user asks \"Which song is your favorite ?\", we find the persona description first and then check the song list, which is exactly like opening the Music App to find the playlist. The existence of this dependency requires us to design a framework to capture the relationships between different sources and decide the call order sequences. **There are two steps that need to be carefully designed: planning and retrieval.** The planning decides the order of sources, once you decide the wrong order, there is no chance to get the correct answer. The retrieval step needs to be executed according to the outputs of planning. We provide additional analysis of the dependency issue in Tables 5 and 7.\n\nB. Thanks for the insightful question. Yes, our framework can be easily extended to encompass additional knowledge sources. Despite our main focus not incorporating more and more knowledge sources, we indeed evaluate the performance of our method in real-world applications by incorporating Wikipedia knowledge or other types of knowledge in a similar setting (dependency and multiple sources). The extent of its effectiveness in specific tasks can vary, and fine-tuning might be necessary to achieve optimal performance for certain applications such as many sources of knowledge are required. Generally speaking, it performs well at planning and assembling different sources of knowledge, especially after in-domain finetuning (user satisfaction improves from 67% to 82%).\n\nC: We mainly follow the method proposed in this paper [3]. For your second question, the answer is yes. The persona config is fixed for each dialogue in our experiments. Since our method is agnostic with the config, it can be modified and updated as the conversation goes on [4].\n\nD: We provide the results in Table 1 by using the method proposed in the FoCus [1] and other methods target multi-sources knowledge-grounding dialogue generation can not be directly applied to KBP (if there is, pls let us know), and we use two Chinese dialogue models CDialGPT [5] and Pangu-Bot [6] as backbones for comparison. We can find that our method performs best and we want to note the FoCus method assumes all responses require knowledge (and the problem setting is also different) thus the comparison may not be fair here (as explained in the response above).\n\nTable 1: The performance between the method proposed in FoCus and SAFARI.\n| Model                      | BLEU1 | RougeL | P.C   | K.C   |\n|----------------------------|-------|--------|-------|-------|\n| CDialGPT + PG + KG   | 22.51 | 25.29  | 64.52 | 17.90 |\n| Pangubot + PG + KG   | 18.06 | 20.23  | 67.20 | 10.41 |\n| ChatGLM (ours) | 23.81 | 26.70  | **76.99** | 42.39 |\n| BELLE (ours)   | **30.48** | **34.61**  | 75.34 | **46.62** |\n\n[1] Call for customized conversation customized conversation grounding persona and knowledge. AAAI 2022\n\n[2] More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge. EMNLP 2021\n\n[3] Long time no see open-domain conversation with long-term persona memory. ACL 2022\n\n[4] Prompted LLMs as Chatbot Modules for Long Open-domain Conversation. ACL 2023\n\n[5] A Large-Scale Chinese Short-Text Conversation Dataset\n\n[6] PanGu-Bot: Efficient Generative Dialogue Pre-training from Pre-trained Language Model", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The paper introduces the KBP dataset, which may be crucial for achieving persona-consistent knowledge-grounded dialogue generation.\n\nThe SAFARI framework, consisting of three steps, is proposed and successfully demonstrates its effectiveness in generating persona-consistent responses.", "reasons_to_reject": "Although the authors declare that the work focuses on multi-source knowledge-grounded dialogue generation, it appears that the knowledge presented in this paper is limited to persona config, including the description and reliant knowledge sentence.\n\nThe knowledge within the KBP dataset is limited to persona-related information, making it suitable only for personalized dialogues rather than general knowledge-grounded dialogues like those based on Wikipedia, such as WoW.\n\nAlthough the data size in KBP is large, the actual knowledge it provides is limited, which may restrict the generalization ability of models trained on this dataset.\n\nThe paper only compares the proposed framework with large language model-based approaches, lacking baselines from previous works on knowledge-grounded dialogue.", "questions_to_authors": "A: Could you please clarify the meaning of lines 302-307 in Section 4.1? How does this particular organization of information influence the follow-up method?\n\nB: The framework shown in Figure 1(b) incorporates multi-source knowledge, including persona config, Wikipedia, and memory. However, in the experiments, only the persona config (persona description and reliant knowledge) was utilized. Have you evaluated the framework using real-world applications, such as incorporating other general knowledge sources?\n\nC: Since the persona config is retrieved, how do you ensure persona consistency? Could it be possible to set the persona config (description and reliant knowledge) as fixed to achieve consistent personalized dialogues?\n\nD: Have you evaluated other knowledge-grounded dialogue generation models using the KBP dataset as baselines for comparison?", "paper_topic_and_main_contributions": "The paper focuses on the problem of multi-source knowledge grounded dialogue generation. To address this problem, the authors propose a personalized knowledge-grounded dialogue dataset called Knowledge Behind Persona (KBP) and a three-step framework called Source Planner for Personalized Knowledge-Grounded Dialogue (SAFARI), based on a large language model. The experimental results on KBP demonstrate that the SAFARI framework can generate persona-consistent and knowledge-enhanced responses.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "Xpv9Dnj9t3", "reviewer": "Reviewer_EWfx", "summary": "", "questions": "* In Table 1, the resp w/ persona\"is 86.1%. What does the other 13.9% represent? Is it NULL or the Knowledge-only case?\n* For in-context learning, are the same examples shown to the LLMs? And how many examples are used?\n* In Table 7, is CHATGLM-6B (the first row) trained in a supervised setting? Shouldn't the values reported be the same as in Table 6 (the second row)?\n* Looking at Table 8, are there responses that deal with more than two documents with persona? If there are no cases using more than two documents in the responses, it makes sense that having more retrieved documents would lead to low performance.\n", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your valuable comments and suggestions. Regarding all of the concerns you raised:\n\n**Reasons to Reject:**\n\n1. We noticed and also cited the related work FoCus as you mentioned. To be clear, there are **three unique differences** between KBP and FoCus datasets: **1) The persona and knowledge in FoCus are more like two independent sources of knowledge, while the knowledge is used to convey information. However, the persona and knowledge in KBP are not independent.** To draw a comparison, the FoCus dataset exhibits a parallel relationship between persona and knowledge, where both sources coexist independently. Conversely, in the KBP dataset, a serial relationship emerges, where the persona chosen significantly influences the knowledge presented, and persona-inconsistent responses can arise even without direct reliance on corresponding knowledge [1]; **2) All responses stored in the FoCus dataset require the involvement of knowledge, but there are three different situations in KBP**: responses without requiring any knowledge; response requires one source of knowledge (PERSONA) and response requires two sources of knowledge (PERSONA and DOCUMENTS); 3) The setting is different. In the Focus dataset, the dialogue system is required to choose one knowledge, and some of the persona or not (multiclass classification for knowledge selection and multi-label classification for persona selection in their original paper). However, as we discussed above, KBP is more focused on the planning of different sources, and also once requiring multiple sources of knowledge, it requires the dialogue system to first choose a persona and then related knowledge.\n\n2. These examples are not from the FoCus dataset since the FoCus dataset does not consider this situation. They are used to illustrate our motivations to propose new settings and found during initial experiments by us and also previous works [1]. We first construct a new dataset to investigate this problem (lines 55 to 77). As we explained above, despite our framework can be applied to the FoCus dataset, the FoCus dataset **can not be used** to investigate the planning capability of LLMs (e.g., decide whether or not requires knowledge, which source to call, and when to call) as it simply always uses knowledge. We compare our method with this kind of strategy in Table 7.\n\n**Questions for The Authors:**\n\n* Yes, the rest 13.9\\% is NULL since there are only three situations in KBP (as illustrated in the caption of Figure 1).\n* Yes, they are the same, and we use three examples since there are three situations (lines 405 to 408).\n* Yes, you are right. We carefully checked all files and experimental logs and we believe this is a typo error due to the tight timeline and extensive analysis. The number in Table 7 and Table 8 is correct and we will revise Table 6 accordingly.\n* Yes, there are only 6 examples in our dataset using more than two documents.\n\n**Typos**\n\nThank you for your suggestions, we will revise it accordingly and hope that our responses can alleviate your concerns.\n\n[1] Like hiking? you probably enjoy nature: Persona grounded dialog with commonsense expansions. EMNLP 2020", "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": "This paper has developed a new persona-knowledge-based Chinese dialogue dataset, KBP. It demonstrates the performance of each step in the proposed SAFARI framework: planning, retrieval, and assembling, and it highlights the effectiveness of these steps.\n", "reasons_to_reject": "This paper proposes a new dataset because there are no sufficient datasets available that can capture the knowledge dependency when generating responses. However, the proposed dataset seems quite similar to the FoCus dataset [1]. When creating the FoCus dataset, both persona and knowledge are also combined in responses. The purpose of the FoCus dataset is slightly more oriented towards conveying information rather than engaging in chit-chat. This might differ from the proposed dataset.\n\nThe examples in Figure 1 (a) and Figure 10 appear to be general failure cases that could possibly happen, but it seems they are not exact examples from the FoCus dataset [1], which weakens your reason for creating a new dataset. The proposed framework, SAFARI, could potentially be applied to the FoCus dataset. To strengthen your work, supporting it with an empirical analysis or providing failure examples of why the FoCus dataset or another dataset is not sufficient for the persona-knowledge-based dialogue generation task will improve your paper.\n(If the examples are directly from a dataset, please let me know)", "questions_to_authors": "* In Table 1, the resp w/ persona\"is 86.1%. What does the other 13.9% represent? Is it NULL or the Knowledge-only case?\n* For in-context learning, are the same examples shown to the LLMs? And how many examples are used?\n* In Table 7, is CHATGLM-6B (the first row) trained in a supervised setting? Shouldn't the values reported be the same as in Table 6 (the second row)?\n* Looking at Table 8, are there responses that deal with more than two documents with persona? If there are no cases using more than two documents in the responses, it makes sense that having more retrieved documents would lead to low performance.\n", "paper_topic_and_main_contributions": "This paper focuses on generating a Chinese dialogue dataset, KBP, where responses depend on the persona alone or both the persona and knowledge. Alongside the dataset, it introduces a supervised and unsupervised framework for large language models, SAFARI, that guides the planning, retrieval, and assembly of knowledge and persona when generating a response.", "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": {"Typos_Grammar_Style_and_Presentation_Improvements": "* In the Evaluation Metric in Section 5.1, \"P.C\" and \"K.C\" should be spelled out since they are mentioned for the first time in the paper."}, "extra_text": {}}, {"review_id": "pOJ5Sf56Lz", "reviewer": "Reviewer_WKZY", "summary": "", "questions": "1. In Tables 6 and 9, it is observed that ChatGPT exhibits lower P.C/K.C/persona consistency scores after the addition of in-context learning examples. Could you provide some insights into why this might be the case? What factors do you believe contributed to this decrease in performance?\n\n2. To further substantiate the necessity of your planning and assembling strategy, have you considered conducting an experiment where ChatGPT is used without these stages? Could you share any findings or observations from such an experiment, if it was conducted?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your valuable suggestions and feedback. We carefully address your concerns one by one as follows:\n\n**Reasons to Reject:**\n\n1. We appreciate the comment and we also acknowledge the organization of different sources of knowledge is valuable as discussed below. However, we would like to clarify that this work focuses more on **the planning capability of LLMs to decide whether or not require knowledge, which source to call, and when to call.** For your problem here for our paper, there are four different ways to organize PERSONA and DOCUMENTS: $a, b, c, d$ as defined below. We provide the comparison results of $a$ and $b$ in Table 7 in the paper and we provide additional results of $c$ and $d$ as you proposed.\n   * $a$: There is a dependency between PERSONA and DOCUMENTS as illustrated in the paper. Suppose N is the size of PERSONA and there are M documents for each persona description in PERSONA (a.k.a., the size of DOCUMENTS is NM), then the time complexity is $O(N+M)$ since we will filter other irrelevant DOCUMENTS in the second step. The space complexity is $O(N+NM)$\n\n   * $b$: There is no dependency between PERSONA and DOCUMENTS as illustrated in the ablation study (Table 7). The time complexity is $O(N+NM)$ and space complexity are $O(N+NM)$\n\n   * $c$: Simply combine PERSONA and DOCUMENTS into one new source. In this way, the size of the new source becomes **the sum of** the size of PERSONA (N) and DOCUMENTS (NM). The time complexity is $O(2(N+NM))$ (2 here refers to the number of retrieved times as explained below) and space complexity is $O(N+NM)$.\n\n   * $d$: Concatenating the individual evidence of PERSONA and DOCUMENTS if they are related. In this way, the size of the new source becomes **the product of** the size of PERSONA and the number of documents for each persona description (M). The time complexity is $O(NM)$ and the space complexity is $O(2NM)$ (here we need to save original N persona NM times, thus 2 NM). The situation becomes worse once the number of sources keeps increasing.\n\n   We use DPR as an encoder and retrieve 2 items in $c$ and 1 item in $d$ for a fair comparison. In $c$, there is a chance that both of the returned 2 items are from PERSONA or DOCUMENTS, we use whether or not the returned results contain the ground persona or knowledge to calculate the metric (**thus this is not exactly recall@1**). The retrieval performance can be found in Table 2. For analysis $a$ and $b$, please refer the Table 7 in the paper. On the one hand, due to introduced noises in $c$, we can find that the performance of PERSONA drops a lot ($83.20 \\rightarrow 54.40$ and $93.07 \\rightarrow 38.67$), and the improvement of DOCUMENTS is caused by the increased number of returned results since both two returned items are from DOCUMENTS. On the other hand, for setting $d$, we can find a slight improvement (less than 1\\%) in PERSONA and DOCUMENTS in Both situations while a relatively bigger drop (more than 2\\%) in the Persona situation. Before we present the performance of assembling, we emphasize that **low efficiency and scalability make the $d$ infeasible** when the number of items in different sources of knowledge increases [5] and the number of sources increases. In addition, it is unnecessary to introduce more information if the response only requires one source of knowledge or part of the information in the concatenation. The time complexity and space complexity can be found in the definition.\n\n\n   Table 3 below shows the assembling results of the above four settings. It can be found there is still a gap between $a$ and $d$, especially at the P.C and K.C metric. We attribute P.C. drop to the low performance at Persona during retrieval and K.C. drop to the use of additional noisy knowledge to generate responses that do not require originally.\n\n\n   Lastly, we want to emphasize our framework SAFARI targets the planning of usages of different sources of knowledge including the response that does not require any source of knowledge. It is not related to how these sources of knowledge are organized, we chose settings $a$ and $b$ mainly based on the novel characteristic of our proposed dataset KBP and their low time complexity and space complexity. Furthermore, RAG, as same as most previous knowledge-grounded methods, makes a strong assumption that all responses require the involvement of external knowledge. However, **it is not held in practice**. For example, there are many other dialogue papers that target non-knowledge-grounded tasks. We target more practical situations in **which the responses do not require, or require one source and even multiple sources of knowledge** by investigating the planning and grounding capability of LLMs. In this way, if we merge multiple sources into single one source, **our framework is still useful to plan (e.g., use knowledge or not), retrieve, and assemble**. Our experiments also center around this motivation and we provide extensive analysis and results to support our claim in the paper.\n\nTable 2: The performance of Retrieval on different settings, where we copy results of $a$ and $b$ from Table 4 in the paper.\n| Settings   | Persona | Both-P | Both-D |\n|------------|---------|--------|--------|\n| $a$ (ours) | **83.20**   | 93.07  | 51.67  |\n| $b$ (ours) | 83.20   | 93.07  | 39.33  |\n| $c$        | 54.40   | 38.67  | 65.33  |\n| $d$        | 80.80   | **93.71**  | **52.11**  |\n\nTable 3: Assembling results of different ways to organize the sources of knowledge in SAFARI.\n| Model      | BLEU1 | RougeL | P.C   | K.C   |\n|------------|-------|--------|-------|-------|\n| $a$ (ours) | **23.81** | **26.70**  | **76.99** | **42.39** |\n| $b$ (ours) | 23.32 | 25.53  | 75.67 | 38.49 |\n| $c$        | 23.16 | 26.44  | 72.02 | 38.56 |\n| $d$        | 23.54 | 26.55  | 74.28 | 39.46 |\n\n2. We argue that the human performance is self-contained. Since our dataset is collected by human annotators, human performance is the best result (or near best) that can be achieved in each metric in both Tables 4 and 6 in the paper.\n\n3. First of all, we want to point out that ChatGLM is also a good LLM, especially in Chinese datasets [3,4]. We chose it to conduct our experiments since it is open-source (Thus we can fine-tune it!) and also inherently incorporates various knowledge in Chinese. Secondly, we provide ChatGPT's performance as you suggested below. We note that the general trends are similar to Table 7 in the paper, such as the metrics of -Planning$^{**}$ are mostly better than -Planning$^*$ except P.C. In addition, the P.C. gap between them is also small same as Table 7. **We emphasize that the first row here can not be regarded as the first row in Table 7 since the model in Table 7 is finetuned.** Furthermore, we want to explain more about the highest performance at P.C. There are two reasons: **1) without the training, the planning and grounding capability is not improved for ChatGPT, thus simple planning strategy may get better results than unsupervised zero-shot ChatGPT** (none of the F1 scores of ChatGPT at planning in Table 4 exceeded 75 without training); 2) There are 1229 samples in the test dataset of KBP of which 181 do not require knowledge, 125 require PERSONA and the rest 923 require both sources of knowledge. In this way, the test setting **is more favorable** for simply using all sources or one source here as they are all majority in the test set (> 75\\%). Based on these observations, we further validate the claim in the paper (lines 451 to 460: unsupervised LLM can not perform well at planning) and the effectiveness of supervised SAFARI which brings higher planning and grounding capability.\n\nTable 4: The ablation studies of ChatGPT under zero-shot setting\n| Model                   | BLEU1 | RougeL | P.C     | K.C   |\n|-------------------------|-------|--------|---------|-------|\n| ChatGPT (zero-shot)     | **12.06** | **24.44**  | 73.47   | **38.00** |\n|  - Planning$^*$    | 8.17  | 20.36  | 78.19   | 24.89 |\n|  - Planning$^{**}$ | 9.01  | 22.57  | **78.60** | 36.69 |\n\n\n**Questions for the Authors:**\n\n1. As explained in lines 486 to 490, the performance of the planning step largely affects the results of assembling. From Table 4, it has been observed that the inclusion of in-context examples does not lead to an improvement in the planning performance of language models, and in some cases, it can even result in a decrease in performance. A similar phenomenon is also reported in previous work [6] as explained in lines 451 to 460. Due to bad planning, the ChatGPT exhibits lower scores.\n\n\n2. Please refer to the above experimental results.\n\n**Missing References and others:**\n\nWe will add it to the revised version.\n\n[1] Long time no see open-domain conversation with long-term persona memory. ACL 2022\n\n[2] Prompted LLMs as Chatbot Modules for Long Open-domain Conversation. ACL 2023\n\n[3] C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models\n\n[4] SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark\n\n[5] There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory. ACL 2022\n\n[6] Knowledge of knowledge: Exploring known-unknowns uncertainty with large language models. 2023.5.23", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper introduces the KBP dataset. It could be a unique resource specifically for personalized knowledge-grounded dialogues. The dataset's focus on responses conditioned on multiple knowledge sources makes it a valuable asset for future research in this area. This could potentially lead to the development of more sophisticated and user-centric conversational AI applications.\n\n2. The authors conduct comprehensive and meticulous experiments, including detailed ablation studies. The reported performance is impressive.", "reasons_to_reject": "1. While the paper's focus on integrating multiple knowledge sources is commendable, it falls short of demonstrating the practical application of this approach. Specifically, it lacks empirical results that showcase the combined use of all knowledge sources into a single one and then use established frameworks such as the RAG for retrieving knowledge from a single source. This omission limits the depth of the study and the potential for comparative analysis with existing methodologies.\n\n2. The absence of human performance benchmarks in Tables 4 and 6 is a significant oversight. Including human performance metrics would provide a more comprehensive understanding of the system's effectiveness and its comparative standing against human-level performance.\n\n3. The paper could have benefited from an exploration of how LLMs such as ChatGPT perform without the planning and resembling stages, especially in the context of Table 7. Given that LLMs inherently incorporate various forms of knowledge.", "questions_to_authors": "1. In Tables 6 and 9, it is observed that ChatGPT exhibits lower P.C/K.C/persona consistency scores after the addition of in-context learning examples. Could you provide some insights into why this might be the case? What factors do you believe contributed to this decrease in performance?\n\n2. To further substantiate the necessity of your planning and assembling strategy, have you considered conducting an experiment where ChatGPT is used without these stages? Could you share any findings or observations from such an experiment, if it was conducted?", "paper_topic_and_main_contributions": "In this paper, the authors introduce a framework that aims to improve dialogue systems by allowing them to incorporate multiple knowledge sources into their responses. Specifically, they explore how LLMs can facilitate the connection between various knowledge sources in the context of personalized knowledge-based dialogue systems. To support this research, the authors created a dataset called KBP which is curated for personalized knowledge-based dialogues. What sets this dataset apart is that the responses are influenced by multiple knowledge sources, creating more engaging and informative conversations that align with the user's knowledge and persona.", "excitement": 2, "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": {"Missing_References": "OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs - Moon et al.", "Typos_Grammar_Style_and_Presentation_Improvements": "When reporting inter-agreement, it is recommended to instead report the kappa value in Table 9."}, "extra_text": {}}, {"review_id": "xEzMVhoKUE", "reviewer": "Reviewer_C1P9", "summary": "", "questions": "1) How do you control the quality of the dataset? What measures do you take?\n2) How do you measure the consistency between different annotators during human evaluation?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive reviews and we will address your concerns one by one as shown in the following:\n\n**Reasons to Reject:**\n\n1. Thank you for your suggestions. We think the size of KBP is relatively large enough (also supported by reviewer qWAp) for current LLMs to learn the planning and grounding capabilities, but we would like to increase the number of sources of knowledge in future work.\n\n2. As the **first work** to explore the planning capability of LLMs in the context of multi-source knowledge-grounded dialogue generation (e.g., decide whether or not require knowledge, which source to call, and when to call), there are **no other suitable datasets** for us to conduct experiments (e.g., the dependency between different sources of knowledge; and contains responses that do not use knowledge and use multiple sources of knowledge simultaneously), Thus we mainly conduct it on our own datasets. We also want to note that the persona and documents in the test datasets **are different** from the training datasets of KBP, which proves the generalization ability of SAFARI.\n\n**Questions for the Authors:**\n\n1. We adopt several methods to control the quality of the dataset: 1) training and pilot annotation (lines 238 to 255); 2) batch collection (lines 256 to 263); 3) We also conduct automatic evaluation and human evaluation to control the quality as introduced in footnotes 7 and 8.\n\n2. As shown in the caption of Table 9, we use inter-agreement to measure the consistency between human annotators.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1) The paper reads well and is easy to follow.\n2) The collected dataset might be a useful resource for the community to do research on personalized dialogue.\n3) The paper proposed a novel framework to address the multiple knowledge sources incorporation issue.\n", "reasons_to_reject": "1) The size of the dataset is somewhat small with only 2477 dialogues. It would be more useful if the author can increase the size of the dataset.\n2) More experiments on other benchmark datasets would be nice to show the generalization ability of the proposed framework.", "questions_to_authors": "1) How do you control the quality of the dataset? What measures do you take?\n2) How do you measure the consistency between different annotators during human evaluation?", "paper_topic_and_main_contributions": "The paper mainly addresses the issue of incorporating multiple knowledge sources and their dependency when generating knowledge-grounded dialogues. The author proposed a framework named SAFARI, leveraging LLMs to first generate the person and relevant documents and then incorporate the retrieved documents and persona into response generation. To study the effectiveness, the author collected a new personalized dialogue dataset called KBP. The experimental results show that the proposed framework is effective in incorporating different knowledge sources.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper presents SAFARI as a framework to leverage the LLM's planning capability to incorporate multiple knowledge sources for personalized knowledge-grounded dialogue response generation. The paper also introduces a new dataset KBP for experiments, and experiments shows the SAFARI is effective at producing persona-consistent and knowledge enhanced dialogue responses. 

While the reviewers generally agreed the paper is well-written and easy to follow, the knowledge within the KBP dataset is limited to persona-related information, making it less clear whether the SAFARI framework can generalize towards general knowledge-grounded dialogues.
Decision: Accept-Findings