Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation
Authors: Gunsoo Han
Daejin Jo
Daniel Wontae Nam
Eunseop Yoon
Taehwan Kwon
Seungeun Rho
Kyoung-Woon On
Chang D. Yoo
Sungwoong Kim
Venue: EMNLP 2023 Findings (2023)
Keywords: knowledge-grounded dialogue generation

Abstract: Knowledge-grounded dialogue generation requires first retrieving appropriate external knowledge based on a conversational context and then generating a response grounded on the retrieved knowledge. In general, these two sequential modules, a knowledge retriever and a response generator, have been separately trained in a supervised manner. However, obtaining intermediate labels of the ground-truth knowledge is expensive, especially in open-domain conversations. Latent variable modeling avoids this need for the labels. In this paper, we propose an efficient algorithm for this latent variable modeling that is able to leverage a large amount of dialogue data. Rather than directly training the complex retriever, we adapt a query generator with an off-the-shelf retriever, and the query generator and response generator are simultaneously trained over the latent variable of query. Moreover, we employ lower bound of the evidence as a training objective and modify it to robustly perform the joint training. Experimental results on diverse knowledge-grounded dialogue datasets show that the proposed algorithm significantly outperforms the supervised learning algorithm even without the use of the annotated knowledge while maintaining efficiency and scalability.

Efficient Latent Variable Modeling for Knowledge-Grounded Dialogue Generation
Gunsoo Han'
Daejin Jo
Daniel Wontae Nam' Eunseop Yoon²
Taehwan Kwon
Seungeun Rho
Kyoung-Woo Woon On'
Chang D. Yoo2 Sungwoong Kim
Kakao Brain
KAAIST KRAFTON GGerriia Institute of Technology
'Korea University
gunsoo hww.kaaaobrain. com
swkim01 ac. kr.
Abstract
et al., 2021). It usually obtains knowledge from ex- ternal resources such as Wikipedia-based databases and web search engines since internal knowledge even in large-scale parametric language models (Brown et al., 2020; Thoppilan al., 2022; Chowd- hery et al., 2022) is incomplete or outdated, and moreover it can provide hallucinated information. In order for dialogue response to be grounded such external knowledge, the conversational agent generally consists of a knowledge retriever, which retrieves knowledge corresponding given dialogue context, followed by response generator that produces informative response based the dialogue context and the retrieved knowledge. In many previous methods, supervised learning has often been applied independently optimize each module using the ground-truth or gold knowledge (Dinan et al., 2018; Shuster et al., 2022a,b; Glass al., 2022; Adolphs et al., 202 la; Nogueira and Cho, 2017; Ma et al., 2020; Xie al., 2022). However, human annotation of knowledge information cumbersome, expensive, and often incomplete due to the existence of multiple pos- sible knowledge candidates and the overlooking of the target response. In addition, existing auto- matic annotations are generally limited to a simple extractive question answering (Zhao et al., 2020; Liu et al., 2021). This difficulty in obtaining anno- tations of knowledge information could be severe under the open-domain conversation and hinders the use of large-scale dialogue data.
Knowledge-groune dialogue generation re- quires first retrieve appropriate external knowledge based on a conversational context and then generate response grounded on the retrieved knowledge. In general, these two se- quential modules, knowledge retriever and a response generator, have been separately trained by supervised data for each module. However, obtaining intermediate labels of the ground-trutt knowledge is expensive and dif- ficult especially open-domain conversation. Latent variable modeling can circumvent and enables a joint training without the knowledge supervision. In this paper, we propose an ef- ficient algorithm
this latent variable mod- eling that is able to leverage a large amount dialogue data. In specific, rather than di- rectly training the complex retriever, we adapt a query generator with an off-the-shelf retriever, and the query generator and response genera- tor are simultancously trained over the latent variable of query. Moreover, we employ the ev- idence lower bound as training objective and modify to efficiently and robustly perform joint training. Experimental results on diverse knowledge- knowledge-rrounded grounded dialogue datasets show that the proposed algorithm achieves state-of- the-art performances even without the use of the annotated knowledge while maintaining the efficiency and scalability.
1 Introduction
Recently, knowledge- grouudde dialogue genera- tion has drawn increasing attention especially as key ingredient for open-domain conversational agents (Dinan et al., 2018; Zhou al., 2018b; Zhan al., 2021; Liu al.. 2018; Zhou et al., 2018a; Lian et al., 2019; Zhao et al., 2020; Kim et al., 2020;
Therefore, there have been a number of recent approaches to learn the knowledge retriever and the response generator without the knowledge super- vision. In specific, they have treated the retrieved knowledge, document, or passage as an unobserved latent variable and adapt latent variable modeling based on approximated marginalization (c.g. top-k) (Lewis et al., 2020; Huang et al., 2021; Cai et al., 2023; Guu et al., 2020), reinforcement learning (Zhao et al., 2020; Zhang et al., 2022; Chen et al.,
Chen et al., 2022; kom; Shuster et al., 2022a,b; Cai
al., 2023; Wu al., 2022: Lewis al., 2020;
Huang et al., 2021: Thulke et al., 2021;
Anantha
Work done at Kakao Brain Corresponding Author
2022: Wu et al., 2022) or variational methods (Zhan

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al., 2021; Paranjape al., 2022: Lian et al., 2019;
retrieval, and (3) a complex posterior sampling. Especially, significantly outperforms previous state-of- of-the-art methods for soowledge-groudeer dialogue generation. In addition, the proposed pos- terior distillation improves the performances over the baseline that solely maximizes the expectation of the conditional likelihood.
Kim et al., 2020; Chen al., 2020; Xu et al., 2023). However, joint training of the retriever along with the generator under this latent variable modeling has some restrictions in utilizing the retriever. For example, a retriever needs to produce differen- tiable prior probability for the gradient propagation through the marginalization or to be intermittently updated to rebuild the whole passage index during training (Karpukhin et al., 2020; Lewis et al., 2020; Zhang et al., 2022). This leads to limitation in fa- cilitating complicated or large-scale retrievers and further in leveraging large-scale data. In addition, the posterior probability used the previous vari- ational methods has been modeled by a separated network, which grows the training complexity and also incurs the discrepancy between the training- time and test-time knowledge samplings.
Our main contributions can be summarized as:
An efficient latent variable modeling is proposed in joint training of query generator and dialogue response generator without the knowledge super- vision for showledge-intensiig dialogue genera- tion.
For realizing efficient yet robust joint training, the ELBO for the marginal likelihood is modified as the combination of the conditional likelihood objective and the posterior distillation objective, based on multiple prior samples.
To overcome such restrictions, in this paper, we propose efficient latent variable modeling, dialogue
The proposed ELVM demonstrates its effective- ness in performing unsupervised joint training and even significant performance improvements over previous methods, being new state-of-the- art on benchmark datasets.
named ELVM, for chowledge-groundee generation. In particular, reduce the burden for training whole retriever by employing a query generator followed by an off-the-shelf retriever that is fixed during training. More precisely, in tandem with the response generator, the query generator rather than the retriever jointly optimized by la- tent variable modeling, in which query is taken as a latent variable, on paired observations of dialogue contexts and responses.
2 Related Work
2.1 Knowledge-Grounded Dialogue
Generation
wowwlege-grounddee conversation has been ex- tensively studied through recently released public datasets (Dinan et al., 2018; kom; Xu et al., 2022b; K Kwiatkowski et al., 2019; Anantha et al., 2021; Moghe et al., 2018). Existing approaches for this task have mostly exploited two successive mod- ules, the knowledge retriever and the knowledge- grounded response generator. Many previous works have used manual annotations of gold knowl- edge provided by some of public datasets to opti- mize the modules (Dinan et al., 2018: Shuster al., 2022a.b; Glass al., 2022; Adolphs et al., 2021a; Nogueira and Cho, 2017: Ma et al., 2020; Xic et al., 2022). Specifically, SeekeR (Shuster et al., 2022a) and BlenderBot3 (Shuster al., 2022b) have recently proposed to build a series of mod- ules by single transformer with different prompts for each module, and trained its transformer on a large number of modular tasks using annotated datasets. However, this manual annotation is expen- sive, time-consuming, and often inaccurate, which impedes the utilization a large-scale dialogue data that is especially necessary for open-domain conversation.
To this end, we exploit the variational method using the evidence lower bound (ELBO) (Kingma and Welling, 2013; Jordan et al., 1999) and approx- imate the expected conditional likelihood in the ELBO by subset sampling from the prior distribu- tion, which acts as the training objective for the response and the query generators. This approxi- mation gets rid of an extra modeling of surrogate posterior distribution or online posterior inference such Markov Chain Monte Carlo (MCMC) and inferenceeeeeeeee discrepancy in
also reduces the knowledge retrieval. Moreover, we further modify the Kuibbeck-Leibler (KL) regularization of the ELBO by constructing the approximated posterior distribution from the conditional likelihood and prior distribution and set this posterior distribution as a teacher for a distillation objective to further learn the query generator.
Experimental results show that the proposed ELVM allows to efficiently and robustly perform training without (1) the use the annotated knowl- edge, (2) an explicit training of the knowledge

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2.2 Unsupervised Joint Training
is important for obtaining appropriate documents. Especially, in knowledge-grounded dialogue, a self-contained query should be generated from the multi-turn dialogue context. A number of prior works have tried train a supervised query rewrit- ing model by human rewrites (Yu et al., 2020; Lin et al., 2020; Vakulenko al., 2021; Voskarides et al.. 2020). Similar to the knowledge annotation, the limitations of manual query annotation have came up with unsupervised learning of the query generator. A number of works have proposed a novel rewards for reinforcement learning of a query rewriter (Wu et al., 2022: Chen et al., 2022).
Recently, unsupervised training methods have been widely applied, and many of them have tries to jointly learn the modules without knowledge labels (Lewis et al., 2020; Huang et al., 2021; Zhao et al.. 2020; Zhang et al., 2022; Zhan et al., 2021; Paran- jape al., 2022: Lian et al., 2019; Kim et al., 2020). For example, PLATO-KA KAG (Huang et al., 2021) has approximated the marginal likelihood by top-k knowledge samples while RetGen (Zhang et al., 2022) has trained to reward knowledge retrieval with the highest utilization in response generation by reinforcement learning and mixture-of-experts ensembling.
QKConv (Cai et al., 2023) has proposed an un- supervised query enhanced method for knowledge- grounded conversation. This work is similar to ours in that it consists of query generator, an off- the-shelf knowledge retriever, and response gen- erator, and unsupervised joint training is applied to the query generator and the response genera- tor. However, their training objective is based the approximated marginal likelihood over candi- date queries, which is different from our objective that includes the posterior distillation based on the ELBO loss.
Mcanwhile, latent variable modeling based on variational methods has also been developed by several recent works. CoLV (Zhan et al., 2021) has introduced collaborative latent spaces to reflect the inherent correlation between the knowledge selec- tion and the response generation. SKT (Kim et al., 2020) has developed sequential latent variable model for the multi-turn knowledge-groune logue generation. In order to perform the posterior sampling of knowledge selection during joint train- ing. some works have proposed to separately train the posterior distribution model (Paranjape al.. 2022; Lian et al., 2019) or the posterior information prediction model (Chen et al., 2020). Very recently, SPT (Xu et al., 2023) has applied short-run MCMC (Erik al., 2019) for posterior sampling on the collaborative latent spaces.
dia-
3 Efficient Latent Variable Model for Knowledge-Gruunee Dialogue Generation
3.1 Setup
In this section, explain overall structure of our ELVM, described in Figure 1a, and define notations. Unlike previous works of document (pas- sage) retrieval (Zhang et al., 2022; Huang et al., 2021: Lewis al., 2020) using query of an em- bedded vector of dialogue context x, we use a text query of natural language, which enables utilize any kind of retriever for dialogue response generation. We define the probability of generating natural language query u for a given dialogue context x. p& (u)22). as below:
While these latent variable modeling algorithms can effectively perform unsupervised joint training. the entire training of the retriever is still difficult and imposes restrictions on the selection of the re- triever in terms of both search algorithm and a knowledge resource. Furthermore, the additional posterior sampling through separate networks or multiple iterations in the previous variational meth- ods also leads to increased training complexity as well as the training-inference discrepancy in knowl- edge generation. In contrast, the proposed ELVM can employ any kind of retriever in latent variable modeling since controls a retrieval output by only changing of a generated query. Moreover, ELVM removes an extra posterior modeling or sampling by prior subset sampling and approximated poste- rior distillation, which leads to efficient training without the discrepancy.
po(u(2)
II (pstu))o0x-1,0))
(I)
where ue is the f-th token in and Ps is the proba- bility obtained from the model parameterized by ø. Similarly, the probability of generating response for given query and dialogue context is defined
2.3 Query Generation
When an off-the-shelf retriever is used for knowl- edge retrieval, how to make an input text query
Po Po Po( (()), x) = II Po( Boor 1, Z(u). (2)

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Raspenacc
Corpus
2(v2)
Entire Query
Ivter sou
Q
2(Un))
Repors
2(4)
Sampling using Po (x))
Genrztar
Beerivved Bocks
Due Fallado
Sampled Query
Aeaan Always
Q*(x) = _ Mg. Mm3
Nenterr
(ONES,
Doe Jost Eaet Tallad
Winned Doad Alno
Retriever
Pg (Mg (g
Query
Quany:
(ex. BM25)
Pg (o((u,)x)po((22(u)...))
Generiter
Ban rock balled
- (x)
(w)) (2,,.,, documents
query
(Utec
Dialogse costent:
What vour Lowelt song
Query
Response
(e())
Generator
Generator
pe
Bon
dialogue context
(b) Example inference of ELVM.
(a) Overall structure of ELVM.
Figure 1: Overall structure and example inference case of ELVM. During training, for given dialogue context x, the query generator generates a set of unique queries Q' and for each query a set of top-k documents Z (u) is retrieved using the off-the-shelf retriever. Then, response generator utilizes both and Z(u): (a) The approximated marginal likelihood Pe, (y(()) used during training is defined by Q and the re- re-normalized prior (u(u)) (b) At inference time, single sampled query is used retrieving multiple documents producing a final response.
where is the t-th token in ((u) {21, 22 .... 2k) } is the set of retrieved documents (passages) by and 0 is the model parameters to produce the response probability. Here, we use top-k result obtained by an off-the-shelf retriever such as BM25 (Robertson et al., 2009) and DPR (Karpukhin et al., 2020) and freeze the retriever. We assume that there is deterministic mapping from to Z. We now set query a latent variable. Then, using the prior (u(r) associated with the query generator and the conditional likeli- hood po ZZ (u), .r) corresponding to the response generator, can define the marginal likelihood of the www.lldge-grouddee response y given the dialogue context x as below:
We construct Q' by obtaining m-unique queries sampled from Pp(-1z)). It is noted that our response generator takes multiple retrieved documents effi- ciently using FiD (Izacard and Grave, 2020), which is different from the use of single document for response generation in previous methods. In addi- tion, while we can utilize relative matching scores from the retriever to assign varying likelihoods to each document given query for (u)())lu) have noticed that this approach hinder the effective and comprehensive learning of the query generator. During the training process, that will be described in the next subsection, our objective amplify the impact of the generated query. Consequently, we opt for deterministic mapping from query set of retrieved docu- ments as unified whole, aiming enhance the gradient flow.
occasionally
(ylc()
Σ po(culr)p(yll(()) uEQ
where Q is the set of all possible queries. Since intractable to marginalize over all queries, we sam- plc a small subset composed m unique queries, (x)x) = { {u,, U22 ..., ) using P=(-Iz) and then marginalize over this set as below:
3.2 Joint Training
Our training objective the ELBO (Kingma and Welling, 2013; Jordan et al., 1999) of log po. which can written as below:
(o)())(=)= = Σ polul=)po(vl((())
log Pa.s (v(z) 2 lo
(4)
uEQ'
-DKL D (0l (6)
where
where qula,, y) is the variational posterior. Note (5) that the equality holds when g(u (z. y) is identical to the true posterior distribution of it. In this work,
Po (u(u))
Po(u|z) =
Eveou (=(

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we do not parameterize g(u) y) and approximate the expected conditional likelihood in the ELBO by sampling u from not ((a[2, y) but po(u)z) such
Overall, the loss for our joint training is
C(0, = (0, (L),
(11)
where 3 the relative weight for the posterior distillation loss.
Bww-tullaw) (w),
~ B Bum (lg Z (u))
(7)
Experiment
This approximation is due to that it minimizes the discrepancy between training and test time infer- ence and it does not separately model q(u|x; y) or complicatedly sample from it during training. Then, we obtain the following two losses corresponding to the expected conditional likelihood and the KL. regularization such as
4.1 Experimental Setup
Dataset. Our experiments are conducted on two widely used knowledge-grounded dialogue datasets, Wizard of Wikipedia (WoW) (Dinan et al., 2018) and QReCC (Anantha et al., 2021). WoW is structured around dialogues that are grounded in knowledge sentences sourced from Wikipedia, con- sisting of roughly 18K, 2K, and 2K dialogues, for training, validation, and test subsets, respectively. QReCC is comprehensive compilation of conver- sational data, consisting of 14K open-domain con- versations comprising 80K question-anse answer pairs.
CV" () puu (u)), (()]),))],))) (8)
(²(0) DxL (
(9)
Moreover, we define qul| 3) in (6) by the approximated posterior derived from the prior and the likelihood such as
pe(y) Pe(y) z()) x))(((( q(u/2,)) En En' (v (w'), () (u'(x)))
(Komeill
351,375
2,467
Query
Fis (xu2)
3,587
392
Wizint (Komeili et al., 2022)
22,488
1,687
(10)
(xu2)
6,279
We set this posterior distribution g(u[z; y) as a teacher for a distillation to update the prior associ- ated with the query generation at test time. Namely, (²)) becomes the posterior distillation loss, and our prior is explicitly updated to be similar to the posterior. Here, the gradient is not propagated from quu2z, v). and q(1(2,,) y) can be computed easily without additional model or approximation since we define it over the subset Q*
Response
Ms Marco (Nguyen al., 2017)
281,636
36,859
NQ Open (Adolphs at.l. 2021b)
79,168
8.757
Wiziot (Komeili et al., 2022)
8,335
Dialogne Response
MSC (Xu 2022a)
105,549
17.691
SaferDinlogues (Ung et al., 2022)
6,306
7,88
PersonaChat (Zhang al.., 2018)
131,438
7,801
Empatheis-ialloues (Rashkin 2019)
64,636
5,738
Table
List of dataset and number of train and val- idation examples used for fine-tuning R2C2 model to obtain R2C2-PT. KG is an abbreviation for knowledge- grounded.
To sum up, obtain the conditional likelihood loss and the posterior distillation loss by multiple samples from the prior distribution that is aligned with query sampling at the inference time. We would mitigate the bias of prior sampling by mul- tiple query samples for each input during training. In addition, our modification of ELBO is differ- ent from the expectation-maximeet rithm in that we sample latent variable from the prior distribution rather than the posterior distri- bution that is used for sampling of E-step in EMM Moreover, contrast to the M-step loss, we use the prior-weighted conditional likelihood and the gra- dient also propagated to the sampled latents and the corresponding prior model. For experiments in this paper, the query generator & and the response generator share parameters, with different input prompts to distinguish each module, for simplicity.
Training. We begin with publicly available R2C2 pre-trained transformer model (Shuster et al., 2022a) with 400M parameter size and fine-tune on multiple datasets to acquire the essential skill sets for knowledgc-grounded dialogue such as query generation and mhowledge-grounde re- sponse generation. Detailed tasks and statisties are shown in Table 1. In line with Shuster et al. (2022b), we adopt a modular system with sin- gle model that incorporates control tokens into the dialogue context to perform different mod- ules. For example, appending the control token generate query__ to the dialogue context promotes the model to generate relevant search query. We name the initial model from this pre- training procedure as R2C2-PT. Subsequently, we apply our proposed algorithm to R2C2-PT model,
(EM) algo-

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Seen
Unseen
Model
PPL
B3
PPL
B3
B4
PostKS (Lian 2019)
79.1
13.0
193.8
13.1
ITDD (Li 2019)
17.8
16.2
44.8
2.1
11.4
SKT (Kim al., 2020)
52.0
19.3
6.8
81.4
16.1
4.2
PIPM (Chen al., 2020)
42.7
3.3
19.9
7.3
65.7
2.5
17.6
5.4
ZRKGC (Li et al., 2021)
40.4
41.5
CoLV (Zhan al., 2021)
39.6
2.9
20.6
7.9
54.3
2.1
19.7
6.3
KnowledGPT (Zhao et al., 2020)
19.2
9.5
7.2
22.0
7.9
22.3
8.3
6.0
20.5
6.7
SPI (Xu al., 2023)
17.1
10.2
7.7
22.7
8.8
19.1
9.6
7.3
22.0
8.5
R2C2
40.7
1.7
0.9
12.3
1.9
46.6
1.6
0.9
11.9
5.4
R2C2-PT
24.7
6.0
4.4
17.3
5.2
30.4
6.2
4.6
17.7
5.4
ELVM-OK
19.8
12.0
9.5
25.3
10.7
25.8
12.1
9.7
25.2
10.5
ELVM-EM-Like
16.2
11.6
9.1
25.7
10.7
19.4
12.0
9.5
25.7
10.8
ELVM-from-R2C2
15.8
14.5
11.8
28.8
13.3
18.9
14.3
11.5
28.8
13.3
ELVM
14.6
14.7
11.9
29.3
13.9
18.0
14.5
11.8
29.2
13.7
Table Performance comparison WoW. PPL represents perplexity,
B4 denote BLEU-3 and BLEU-4
scores, respectively, and RI and R2 indicate Rouge-1 and Rouge-2 scores, Our proposed model, ELVM, achieves new SOTA performance for both seen and unseen tasks with substantial margin.
Seen
Unseen
Model
FL
EM
RIT
R21 PPL.
Model
R@1
R@5
R@10
R@1
R@5
R@:0
Q ReRwriting (Raposo et al., 2022)
18.9
1.0
R2C2-PT
25.7
14.3
29.2
DPR-IHN (Kim and Kim, 2022)
30.4
ELYM-EM-Like
26.4
63.9
14.7
28.9
75.8
QKConv (Cai 2023)
ELVM-3=0
26.1
64.6
75.7
28.9
65.3
76.8
R2C2-PT
22.8
25.7
11.5
BLVM
29.1
68.4
80.3
30.4
68.3
80.3
ELVM-OK ELVM
33.0
4.5
35.6
21.9
5.6
36.5
39,0
25.8
48
Table 3: Comparison of document recall in WoW he- tween ELVM variants. Note that we omit the perfor- mance of ELVM-OK whose performance is identical to R2C2-PT since is trained with keeping its query generator frozen.
Table 4: Performance comparison in QReCC with QK- Conv (Cai al., 2023) which is the previous SOTA on this task. We report Fl scores, exact match (EM). Rouge score (R) and perplexity (PPL).
Fluency (%)
Relevance (%)
Model
Scen
Unscen
Seen
Unscen
resulting in ELVM. Furthermore, dispel concerns that training gains might predominantly stem from R2C2-PT'ss comprehensive fine-tuning across multiple datasets rather than ELVM's inherent ef- ficacy, we opted utilize the base R2C2 model. Upon integrating with ELVM. the resulting con- figuration is denoted RLVM
KnowledGPT
69.8 8
62.9
41.5
41.1
ELVM
73.5
77.5
49.5
63.6"
Table 5:
Human evaluation results WoW test. A pair- wise t-test conducted to verify statistical significance of the improvements, and the corresponding results in bold are significantly better than those from the baseline model (*": p < 0.01. *: p 0.025).
During the training of ELVM, employ sampling-based generation method, specifically cleus sampling (Holtzman al., 2020), promote diverse query generation in Ps- When retrieving documents based on the generated queries, uti- lize an off-the-shelff sparse retriever such as BM25 (Robertson al., 2009), prioritizing low latency over the dense retriever (DR). We train for 3 epochs with m and & 5 for WoW and epoch with 8 and 10 for QReCC. /3 is both tasks, unless stated otherwise. To ensure thorough evaluation of ELVM's performance and its ability to generalize to unseen dialogues, we purposely avoid dataset overlap between training of R2C2-PT and ELVM. More detailed informa- tion on the training process can be found in Ap- pendix
Inference. During inference, unlike the training phase, only single query is generated by the query generator Ps. The default number of retrieved doc- uments k from the off-the-shelf retriever for WoW and 10 for QRcCC. The knowledge- grounded response generated by the response generator, Pp- An illustration of the inference is depicted in Figure 1b. During the inference, both query and response generation are conducted using beam search with beam size of 5.
for
To examine the impact of the proposed ELVM training, we explore another vari- ant, ELVM-OK, which is trained on the response generation task with annotated oracle knowledge documents while keeping the query generator Ps
Variants of ELVM.

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Seen
Unseen
Wow Unseen
QRcCC
B4
R21 1
B41 f
R21 1
Model
B4
R2
R@5
1
f
1
R@5
10.8
12.4
10.5
12.2
BLMM
11.8
13.7
ML
12.8
15.5
2
11.1
13.0
11.1
12.8
4"
11.9
13.9
11.8
13.7
Table 8: Performance results achieved by employing the dense retriever instead of the default BM25 retriever for WoW unseen and QReCC evaluation.
8
11.8
13.7
11.7
13.6
Table 6: Effect of varying the number of queries, m, sampled during training on WoW. We see the highest performance on unseen task when m = 4. The super- scripted value by is the default setting for ELVM.
Scco
Unseen
Model
B41
R2
R@5 1
B4
R2 1
R@5
R2C2-PT
4.4
5.2
63.8
4.6
5.4
64.8
ELVM-OK
9.5
10.7
9.7
10.5
ELVM
11.9
13.9
68.4
11.8
13.7
68.3
Wow Unseen
QReCC
R2C2 PTume
8.7
10.2
62.9
8.9
10.3
64.4
8
B4
f
R2 f
R@51
F1 1
EM f
R@5
ELVM- ELVM-OKEam
9.6
10.6
10.2
11.2
0
9.3
10.6
65.3
34.5
5.1
55.8
VM
12.6
14.3
72.6
12.4
14.1
74.3
1"
11.8
13.7
68.3
36.5
6.9
65.6
5
10.8
12.4
66.7
31.3
3.5
31.9
Table 9:
Effect of model scaling tested on WoW. ELVM scales out larger parameter size with increase in per- formance. The subscript Large indicates the model size of 2.7B parameters.
Table 7: Ablation study of the posterior distillation weight /3 on WoW. From the results, we can see that the trivial value of 8=1 shows the best performance. The super-scripted value by is the default setting for ELVM.
increase is predominantly attributable to ELVM. We further examine the performance of query generator by assessing the recall of the ground-truth documents. The results summarized in Table 3 reveal that training with our proposed algorithm leads to increase in recall for both ELVM-3=0 and ELVM compared to R2C2-PT while the lat- ter yields larger performance gain. This high- lights the effectiveness of distilling the posterior distribution of the response generator into the prior distribution associated with the query generator. For QReCC, as shown in Table 4, ELVM sur- passes the previous SOTA, QKConv (Cai et al., 2023), the recent previous works (Raposo et al., 2022: Kim and Kim, 2022), and the variants of ELVM including R2C2-PT and ELVM-OK in terms of all metries. The recall performance on QReCC is included in Table C.
frozen. During inference, given a dialogue context the frozen query generator Ps generates query and use the off-thh-shelf retriever to bring relevant docu- ments. Finally the response generator of ELVM-OK generates the final response.
In Section 3.2, we describe the difference be- tween ELVM training and EM algorithm. In order to quantitatively compare these two meth- ods, we perform EM-like training in ELVM where we apply the posterior-weighted condi- tional likelihood in CV, 6): LV.((0 (0,g) o)
- Eu (Gl((u y)) log po (u), æ². Here, the query latents u. are still sampled from the prior distribution, and SG means the stop-gradient that prevents the gradient propagation to the query gen- erator. We name this variant as ELVM-EMMllike
4.3 Human Evaluation
4.2 Automatic Evaluation
To gauge performance across multiple aspects of the quality of generated responses, human evalu- ation is performed on the generated responses ELVM and KnowledGPT (Zhao et al., 2020)'. Sim- ilar to (Rashkin et al., 2021) and (Xu et al., 2023), we assess the response quality in two aspects: Flu- ency and Relevance. Fluency measures whether the response is understandable, self-consistent without repetition, and proficient. Relevance assesses the extent to which the response aligns with the dia- logue context, incorporates pertinent knowledge, and maintains appropriateness. In total, 50 data
The evaluation results of WoW test seen and un- seen tasks are shown in Table ELVM surpasses all its variants and previous state-of-the-ar (SOTA) model, achieving a new SOTA on both WoW test seen unseen tasks, across all metrics. addition, ELVM-OK demonstrates notable improvements BLEU and Rouge scores compared to previ- ous models with slight drawback in PPL. Sim- ilarly, ELVM-EM-Like also cxhibits competitive performance across multiple metrics. Moreover, when comparing WVM--foommRR and ELVM, the experiment demonstrates that while R2C2-PT offers marginal benefits, the marked performance
We choose KnowledGPT because it the best performing model among publicly available models.

---

Seen
Unseen
performance observed when is increased to 5 or set to O, particularly in the case of QReCC.
Model
844 1
R2 1
B4 1
R2 1
CoLV
39.6
2.9
7,9
54.3
2.1
6.3
KnowledGPP
19.2
1.2
22.3
SPI
17.1
7.7
8.8
19.1
7.3
8.5
Varying the Retriever. We extend our exper- iments by incorporating the DR in two distinct ways. For WoW, we train and evaluate using all- Mi Mini1M-L6-v2 DR. In contrast, for QReCC, due to its extensive knowledge pool, we leverage DR solely during evaluation by employing both the BM25 retriever and all all-mpret -baseee2bb DR in a hybrid manner to retrieve the top-k documents. Concretely, we score each document by summing up the normalized score of each retriever and re- rank to obtain top-k documents. Table 8 presents a summary of the performance results of employing DR, demonstrating successful integration of DR in different settings of ELVM.
BART
974
1.9
2.6
973
1.3
2.6
ELVM-from- BART
BLVM
14.6
11.9
13.9
18.0
11.8
13.7
Table I0: Performance comparison with BART
initial model.
WoW Unseen
Model
Train Rei
Test Rei
PPL 4
B4 1
R2
Fl
CoLV
54.3
2.1
6.3
18.5
KnowledGPT
SPI
19.1
7.3
8.5
ELVM
BM25
BM25
18.0
11.8
13.7
27.0
BLYM
BM25
Dense
14.8
12.6
15.2
29.3
ELVM
Dense
Dense
12.9
12.8
15.5
29.0
ELVM
Deuse
BM25
21.6
11.8
13.1
28.2
Table 11: Impact of alternating the retriever between training and testing phases on WoW unseen test. Dense indicates utilizing all-Minil.M--L6-y retriever de- scribed in Section 4 and Ret is an abbreviation for re- triever.
Scaling Up Model. We investigate the impact of increasing the model size on performance. Start- ing with the publicly available 2.7B R2C2 pre- trained transformer, we follow exactly the same procedure outlined in Section 3.2 to obtain R2C2- PT Large. ELVM-OKLarg and ELVgLarge Detailed hyperparameters for training 2.7B model is given in Appendix A. The results presented in Table 9 demonstrate that the model size increases, there is improvement in performance across all met- rics, supporting the scalability of ELVM.
samples are randomly selected from WoW tasks where 25 samples are randomly selected from each seen and unseen task. The qualities of the responses are measured by A/B testing on the two aspects. The feedback is collected from 11 human experts. Further details and annotator instructions can be found in Table G. As shown in Table 5, ELVM sig- nificantly outperforms KnowledGPT in all aspects especially on the unseen task.
BART Base. To ensure fair comparison and further demonstrate the robustness of our algorithm on diverse model scales, we conduct an experiment employing BART (Lewis et al., 2019) base as an ini- tial model and then apply our proposed algorithm on WoW dataset. We choose BART base model since the previous SOTA model (Xu et al., 2023) utilized BART base as their base model. The out- come of this training termed ELVM-from-BART As shown in Table 10, the initial BART model shows very poor performances. However, upon training the BART-base with our ELVM algorithm. there is a great improvement performance across both WoW Seen and Unseen tasks. Importantly, the results surpass the performance benchmarks set by the previous SOTA model (SPI).
4.4 Ablation Studies
The size of sampled query set Q³, m, plays huge role in training of ELVM, as it directly influences the bias of the marginal like- lihood of the knowledge-grounde investigate the impact of m, we gradually increase from 1 to 8 and measure the performance WoW while keeping other hyperparameters fixed. As shown in Table 6, we observe positive correla- tion where increasing m leads to improved perfor- mance. Notably, setting m 4 yields the optimal performance for both seen and unseen tasks.
Number of Queries.
response y. To
Posterior Distillation Weight. We examine the influence of the posterior distillation loss on ELVM training by adjusting the value of 3. The experi- mental results presented in Table 7 reveal that the optimal performance attained when 3 for both the WoW unseen and QReCC tasks, indicat- ing the effectiveness of incorporating the posterior distillation loss. However, a substantial decline in
Retriever Switching To assess the robustness of our algorithm to the retriever mismatch between training and inference phases, we undertake an
Minil M Sttgss//nngiiiiicccssssscmmmmmmm
hasc-v2

---

want teach my family about investing by buying them few shares of stock, but really don't know much about
Dialogue Context
The stock market also be called the equity market share market. That is where you buy and sell stocks. The stock represent ownership claims on businesses.
Oh, so I could tell my kids they own part of company. That's cool. Maybe should get them share in company they know then, like Apple or XBox or Snapchat?
GT knowledge
[... Examples of the latter include shares private companies which are sold investors through
equity crowdfunding platforms. [..]
GT Response
Yes, it's like the shares of a company sold to investors through equity crowdfunding platforms.
GT knowledge selected: False Respouse: yes, you can get shares in company like apple xbox. you can also get shares
Query: n/a
KnowledGPT
company like stock market.
GT knowledge selected: False Response: The stock market can also be called the equity market or share market. That is where you buy and sell stocks. The stock represent ownership claims on businesses.
Query: How to teach kids about investing
R2C2-PT
GT knowledge selected: False Response: That's ' good idea. The stock market is place where you can buy and sell stocks.
Query: Investing tips for beginners
ELVM-3 =
Query: Apple xbox snapchat stock share buy apple stock instagram
GT knowledge selected: True
stock investing
ELVM w/ BM25
Response: That is a good idca. You can also buy shares of private companies which are sold to investors through equity crowdfunding platforms.
GT knowledge selected: True Response: That is good idea. The latter include shares of private companies which are sold to investors through equity crowdfunding platforms.
Query: Best stocks for teaching kids about investing
ELVM w/ DR
Table 12: Generated samples from WoW unseen task. The first represents the dialogue context, ground truth (GT) knowledge (highlighted in olive), and GT response. Following rows contain the model generated query. response and whether the GT knowledge is included in top-5 retrieved documents. More samples can be found in Appendix H. We truncate the GT knowledge and display only relevant spans of the GT knowledge to the ground-trute response.
additional experiment, of which the results are de- picted in Table 11. In this experiment, we train the WoW task utilizing either BM25 or dense re- triever (utilizing all-MiniLM-L6-22 retriever, as detailed Section 4), and subsequently evaluate us- ing either BM25 or dense retriever. The results on the WoW unseen test indicate that utilizing different retriever during testing doesn't 't necessitate model re-training, particularly given the superior performance of the dense retriever compared to BM25.
Conclusion
In this paper, we propose ELVM as an efficient latent variable modeling for knowledge-intensiv dialogue generation. In ELVM, the knowledge re- trieval is realized by the query generation followed by document retrieval using the off-the-shelf re- triever for ease of joint training of both the retriever and the response generator, without the knowledge supervision. Furthermore, the training scheme of ELVM modifies the ELBO for the marginal likelihood to effectively perform the joint train- ing without the use of complex posterior sampling, which also eliminates the discrepancy between the training-time and test-time knowledge samplings. ELVM empirically demonstrates that it significant outperforms over previous latent variable methods with in-depth analysis on diverse dialogue datasets.
4.5 Qualitative Analysis
For qualitative analysis, we examine the generated samples from KnowledGPT, R2C2-PT and other variants of ELVM, whose samples are presented in Table 12. The example reveals that the queries gen- erated by KnowledGPT, R2C2-PT and ELVM--9=0 fail to retrieve the relevant knowledge effectively while the queries generated by ELVM with BM25 and DR
Limitations
There are a number of limitations and possible di- rections to future works of ELVM proposed in this paper. First, as more large language models be- yond the scale of hundreds of billion parameters are emerging, there are rooms for emergent behaviors in ELVM as the sizes of data and model grow that
L6 L2- reflects the abil- ity to access the relevant knowledge, leading to improved responses. The results also demonstrate that the query generator trained with our algorithm effectively adapts to the chosen retriever.

---

has not been tested in this paper. Second, instead of the prior sampling that we use. fast posterior sam- pling such as short-run MCMC (Erik et al., 2019; Xu al., 2023) also an alternative candidate. Third, the experiments in this paper retrieve doc- uments from confined set (e.g.. Wikipedia) using light retriever such as BM25 and dense retriever. However, real-world applications often rely on the web which has much bigger pool of documents from broader open-domain. In turn, we should consider future extension of this work to utilizing larger public search engines. Last, we may con- sider alternative yet more effective means of query sampling during training other than a small set of random samples, as the current design. Other possi- ble methods include imposing additional guidance for query sampling or multiple refinements of query sampling and knowledge retrieval for more diverse set.
Leonard Adolphs, Kurt Shuster, Jack Urbanek, Arthur Szlam, and Jason Weston. 2021b. Reason first, then respond: Modular generation for knowledge-iffused dialogue. arXiv preprint arXi:2111.052000
Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, and Srinivas Chappidi. 2021. Open-domain question answering goes conversational via question rewriting. In Pro- ceedings of the 2021 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, Online. Association for Computational Linguistics.
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie 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, llya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. arXiv preprint arXiv:2005. 1416:.
Ethics Statement
This work presents no direct ethical issues. How- ever, there is a possibility that the generated re- sponses may inadvertently contain inadequate, bi- ased, discriminatory content. We do not intro- duce new datasets, and all experiments are con- ducted using publicly available datasets.
Mingzhu Cai, Siqi Bao, Xin Tian, Huang He, Fan Wang- and Hua Wu. 2023. Query enhanced knowledge- intensive conversation via unsupervised joint model- ing, In ACL.
Xiuyi Chen, Fandong Meng. Peng Li, Feilong Chen, Shuang Xu, Bo Xu, and Jie Zhou, 2020. Bridging the gap between prior and posterior knowledge lection for mhowledge-groundee dialogue generation. In EMNLP, pages 3426-3437.
Acknowledgments
This work was supported by Institute of Informa- tion& & communications Technology Planning & Evaluation (IIP)) grant funded by the Korea gov- ernment (NS 2019-0-00079, Artificial In- telligence Graduate School Program, Korea Uni- versity). In addition, this work was also supported by Artificial intelligence industrial convergence cluster development project funded by the Min- istry of Science and ICT(MSIT, Korea) Gwangju Mctropolitan City. Finally, would also like to thank Brain Cloud Team at Kakao Brain for their support.
Zhiyu Chen, Jie Zhao, Anjie Fang, Besnik Fetahu, Oleg Rokhlenko, and Shervin Malmasi. 2022. Reinforced question rewriting for conversational question an- swering. arXiv preprint arXiv:2210.15777
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sehastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vin- odkumar Prabhakaran, Emily Reif, Nan Du Ben Hutchinson, Reiner Pope, James Bradbury. Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito. David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov. Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaramarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Mor- cira. Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022. Palm: Scaling
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A Training Details
Parameter
Value
Training Steps
10,000
Batch Size
Optimizer
AdamW
Jing Xu, Arthur Szlam, and Jason Weston. 2022a. Be- yond goldfish memory: Long-term open-domain con- versation. In Proceedings of the 60th Annual Meet- ing of the Association for Computational Linguistics (Volume I: Long Papers), pages 5180-5197, Dublin, Ireland. Association for Computational Linguistics.
Learning Rate (LR)
Se-5
LR Scheduler
Cosine
Adam 3
0.9
Adam 82
0.999
Adam
1e-8
Warmup Steps
1000
Jing Xu, Megan Ung, Mojtaba Komeili, Kushal Arora, Y-Lan Boureau, and Jason Weston. 2022b. Learning new skills after deployment: Improving open-domain sneemet-drivony dialogue with human feedback. arXiv preprint arXiv: MXXi::228.0032777
Table 13: Hyperparameters used for stage-1 training. k indicates number retrieved documents.
Yan Xu, Deqian Kong, Dehong Xu, Ziwei Ji, Bo Pang. Pascale Fung, and Ying Nian Wu. 2023. Diverse and faithful knowledgo-groumdg dialogue generation via sequential posterior inference.
WoW
QReCC
Train Epochs
Batch
Optimizer
Adam W
Adam W
Learning Rate (LR)
le-4
5e-4
Shi Yu, Jiahua Liu, Jingqin Yang, Chenyan Xiong, Paul Bennett, Jianfeng Gao, and Zhiyuan Liu. 2020. Few- shot generative conversational query rewriting. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Informa- tion Retrieval, pages 1933-1936.
LR Scheduler
Cosine
Cosine
Adum 81
0.9
0.9
Adam 82
0.999
0.999
Adam
Ic-8
Ic-8
Warmup Steps
100
Min Query Length
Haolan Zhan, Lei Shen, Hongshen Chen, and Hainan Zhang. 2021. Colv: A collaborative latent variable model for knowledge-groundee dialogue generation. In EMNLP.
Max Query Length
Top-k
100
100
Top-p
0.95
0.5
Temperature
1.0
2.0
Saizheng Zhang, Emily Dinan, Jack Urbanck, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Per- sonalizing dialogue agents: I have a dog. do you have pets too? Proceedings of the 56th Annual Meet- ing of the Association for Computational Linguistics (Volume I: Long Papers), Melbourne, Australia. As- sociation for Computational Linguistics.
Table 14: Hyperparameters used for training ELVM and its.
Table 13 we provide hyperparameters utilized during this training process. We also report hyper- parameters used for training ELVM and its variants model (ELVM-OK and ELVM-EM-Like) in Ta- ble 14. In addition, during training of QReCC, we adopt a strategy to decrease the knowledge pool size from 54M to 1M. This reduction aims to miti- gate latency issues during document retrieval. Con- cretely, the 1M pool is constructed by initially gath- ering the ground-truth relevant passage for each instance in the training dataset then adding ran- domly sampled documents. During the evaluation stage, utilize the original full-size knowledge pool of 54M passages.
Yizhe Zhang, Siqj Sun, Xiang Gao, Yuwei Fang, Chris Brockett. Michel Galley, Jianfeng Gao, and Bill Dolan. 2022. Retgen: A joint framework for retrieval and grounded text generation modeling. In AAAI.
Xucliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, and Rui Yan. 2020. Knowledge- grounded dialogue generation with pre-trained lan- guage models. In EMNLP.
Hao Zhou, Tom Young, Minlic Huang, Haizhou Zhao, Jingfang Xu, and Xiaoyan Zhu. 2018a. Common- sense knowledge aware conversation generation with graph attention. national Joint Conference on Artificial Intelligence, LICAI' 18, page 4623 4629. AAAI Press.
Proceedings of the 27th Inter-
B Upper Bound Performance
We believe in the value of understanding the utmost capability in an ideal scenario. To this end, we pursue an upper-bound performance analysis for both the WoW and QReCC tasks. This approach
Kangyan Zhou, Shrimai Prabhumoye, and Alan W Black, 2018b. dataset for document grounded conversations. In EMNLP.

---

Seen
Unseen
Model
P
B3
B4
1
PPL 4
B3
B4
1
FLVM
14.6
14.7
11.9
29.3
13.9
18.0
14.5
11.8
29.2
13.7
ELYM-w-GT-Doc
9.7
22.1
18.5
40.9
23.9
10.5
21.5
17.9
40.7
23.3
Table 15: Performance comparison in WoW in the presence of ground-truth document during evaluation (ELVM-w- GT-Doe).
Model
FI 1
EM
Seen
Unseen
Q.Rewriting (Raposo et al., 2022)
18.9
1.0
Model
B4
R@5
R 10
R21
R@5
10
DPR-IHN (Kim and Kim, 2022)
30.4
4.7
11.9
68.4
80.3
13.7
80.3
QKCony (Cai et al., 2023)
33.5
5.9
62.7
73.9
ELVM
36.5
6.2
64.8
76.4
10uue
ELVM-w-GT-Doc
55.4
16.2
Table 18: Efffect of increasing the document pool size
Table 16: Performance comparison in QReCC the presence of ground-trutl document during evaluation (ELYM.w-GT-Doc).
1000 the performance of ELVM. As hefore, kt indicates the number of retrieved documents used during train and inference.
involves training our response generator under the assumption that the model has access to the ground- truth document during both training and evaluation. The performance metrics of this specific setup designated as "ELVM-w-GT-DE
D Knowledge Pool Scaling
In order create more realistic and challenging retrieval scenario, expand the original setting of the WoW dataset by increasing the number of relevant documents per instance 1000. This mod- ification better reflects real-world information re- trieval scenarios where vast array of both relevant and irrelevant documents are typically encountered. We construct this extended dataset by randomly augmenting 1000 K documents from the WoW dataset to the document pool for each instance, where K represents the number of annotated rel- evant documents for each instance. On average, K A 20 for each instance in WoW dataset.
in Table 15 and Table 16. From both tables, it's evident that there still remains margin for improvement for both tasks.
C Document Recall Performance
Query from
R@1
R@5
R@ 10
Dialogue History
33.4
58.6
68.7
Last Utterance
25.5
50.8
62.6
R2C2-PT
17.4
33.0
39,5
Results in Table 18 show performances of ELVM models with different number of retrieved docu- ments, k, and knowledge pool size, where mod- els with increased knowledge pool has tailing key- word Bulk at then end of their names. Despite the increase in complexity of the retrieval task, ELVM demonstrates robust performance, with only marginal drop in metrics compared to the original setting. Furthermore, it is important to highlight that even under these more difficult conditions with 1000 relevant documents, ELVM still outperforms SPI by significant margin.
ELVM-8=0)
30.6
55.8
65.7
ELVM
35.2
$9.9
68.6
ELVMOR
37.5
65.6
75.1
Table 17: Query performance comparison in QReCC for different types of queries.
We report the document recall performance on QReCC, including different types of queries, as show in Table 17. Our proposed model, ELVM. passes both its variants and non-model generated queries such as using dialogue context as a query. The document recall performance on QReCC, en- compassing various query types, presented in Table 17. Our proposed ELVM model outperforms its variants as well as non-model generated queries, including the use of dialogue context as a query. Moreover, incorporating the dense retriever for re- ranking documents (ELVMpr) leads further im- provements recall.
E Effect of Parameter Sharing
In our ELVM training, parameters are shared be- tween the query and response generators. To probe this design choice, we decoupled these parame- ters, resulting the ELyM-Decouple configura- tion, as detailed in Table 19. Upon evaluation on WoW's test seen and unseen tasks, ELVM-

---

Seen
Unseen
Model
PPI.
B4
R21
PPI.
B4
R2
ELVM
14.6
11.9
13.9
18.0
11.8
13.7
ELVM-Decouple
16.2
12.6
14.5
21.6
12.2
Table 19:
Performance comparison on the WoW dataset using the ELVM algorithm. ELVM denotes training with shared parameters between query and response gen- erators, while ELVM-Deccuple denotes a non-shared parameter approach.
Seen
Unseen
Model
F1 1
KFI
F1
KFI
CoLV
20.3
18.2
18.5
17.5
KnowledGPT
22.0
23.8
20.5
22.1
SPI
R2C2-PT
15.4
20.5
15.7
22.3
BLVM-OK
23.0
33.0
22.9
33.0
ELVM-EM-Like
24.5
33.2
23.5
33.5
ELVM
27.2
40.8
27.0
40.3
Table 20: models.
Fl and KF1 scores for ELVM and baseline
Decouple showed a slight edge over the standard ELVM. Nevertheless, the benefits of shared param- eters portspaatilllallly regarding computational effi- ciency and knowledge transfer during training and inference-camee cannot be understated.
F Additional Metrics
We report the evaluation of models on the Wow task using both Fl and knowledge Fl (KF1) metries in Table 20. For reference, values pertaining to previous models are derived from Sun al. (2023).
Human Evaluation
For a comprehensive evaluation, we conduct the hu- man evaluation between ELVM and KnowledGPT on seen and unseen tasks in WoW (Dinan et al., 2018). We randomly select 50 samples (25 sam- ples per task), and each sample is evaluated by 11 different human experts. In specific, the two generated responses from each model in the same context assigned to the amnotators. For A/B test- ing, give score the model if it's response is received equally good or better than the other one. Figure 2 shows the annotator instructions for the two aspects.
H Generated Samples
Generated samples for the WoW test unseen are presented in Table 21 and Table 22. Similarly, Ta- ble 23 and Table 24 showcase the generated sam- ples for the QReCC test.

---

Dialogue Context
Hey, do you like cardigans?
like the modern version without no buttons and hangs open by design.
Me too. My fav is the waterfall style but it's going of fashion I think
GT knowledge
[...] Cardigan (sweater) By contrast, a pullover does not open in front but must be "pulled over" the
head to be worn. [...]
GT Response
But the pullover does open in front but must be "pulled over" the head to be
Query: n/a
KnowlcdGPT
GT knowledge selected: False
Response: like the hand-knitted version. it's more comfortable. like mine made of a cardigan.
Query: fallen fashion
R2C2-PT
GT knowledge selected: False
Response: I' m sorry, didn't 't mean to offend you.
Query: fallen fashion cardigans
ELVM-3
GT knowledge selected: True
Response: like the one that is tied is instead considered robe.
Query: fallen waterfall eardigan style fallout cardigans
ELVM w/ BM25
GT knowledge selected: True Response: like the pullover style that does not open in front must be "pulled over" head to worn.
Query: fallen waterfall cardigan
ELVM w/ DR
GT knowledge selected: True
Response:
I like the pullover that does not open front but must be "pulled over" the head to be worn.
Dialogue Context
[..]] Was she diagnosed as a child? It is much easier to diagnose children, since the symptoms begin occurring early in life
Yes it started when she was years old. She 23 when met her she already recieving treatment
GT knowledge
[.. ] These fellowships include pediatric neurosurgery, trauma/neurocritical care, functional and stereotactic surgery, surgical neuro-oncology. radiosurgery, neurovascular surgery, skull-base surgery, peripheral nerve and spine surgery. [....
GT Response
My first fellowship
pediatric neurosurgery. is great that she is doing better.
Query: n/a
KnowledGPT
GT knowledge selected:
False
Response: did she cry during the periods of depression? she was crying. negative outlook on life, and poor eye contact with others.
Query: types of epilepsy
R2C2-PT
GT knowledge selected: False
Response: am glad to hear she is receiving treatment. Dementia is such a devastating discase.
Query: types epilepsy
ELVM-3
GT knowledge selected: False
Response: am glad she receiving treatment. Dementia has significant effect on a person's caregivers.
Query: neurosurgery epilepsy neurocognitive neurosciences
ELVM w/ BM25
GT knowledge selected:
True
Response: glad to hear that is receiving treatment. Neurosurgery is branch of medicine that specializes the treatment epilepsy.
Query: neurosurgery treatment for epilepsy
ELVM w/ DR
GT knowledge selected:
True
Response: Did she undergo fellowship? Many neurosurgeons pursue additional training in the form of fellowship, after residency in some cases, senior resident.
Table 21: Generated samples from WoW test unseen. We truncate the ground-truth knowledge and display only relevant spans of the ground-truth knowledge to the ground-trun -ruuhthtthhhhh response.

---

Dialogue Context
you anything about the band called Insane Clown Posse?
Insane Clown Posse (ICP) is an American hip hop duo composed of Violent J ,ad Shaggy 2 Dope.
[..]
That's quite lot of albums, what else do know about ICP?
GT knowledge
[.. The duo founded independent record lahel Psychopathic Records with Alex Abhiss as manager, and produced and starred in feature films "Big Money Hustlas" and "Big Money Rustlas", [... The duo founded the independent record label Psychopathic Records with Alex Abbiss manager, and produced and starred in the feature films "Big Money Hustlas
GT Response
Respouse: Query: n/a
KnowledGPT
GT knowledge selected: False
Response: the duo has carned two platinum and five gold albums. they have also carned two platinum and five gold albums
Query: ICP wiki
R2C2-PT
GT knowledge selected: False
Response: duo has earned two platinum ,wtth golden album
Query: ICP facts
ELVM-3 = 0
GT knowledge selected: False
Respouse: group has been around for long
Query: Insane clown posse ICP facts
ELVM w/ BM25
GT knowledge selected: True
Response: The duo Founded the independent record lābel Psychopäthic Records with Álex Abbiss manager, and produced and starred in the feature films "Big Money Hustsas"
Query: Insane clown posse facts
ELYM w/ DR
GT knowledge selected: True
Response:
The duo founded the independent record label Psychopathic Records with Alex Abbiss as manager, and produced and starred in the feature films "Big Money Hustlas"
Dialogue Context
[...] Athletes normally meat large quantities. Exactly. I have also added much meat eating as of my special diet. They are rich in protein. Especially, red meat
GT knowledge
[... nutritional science, than white meat, white meat being defined non-dark from chicken (excluding leg thigh), or fish. [...
other hand, red meat is defined as any meat has more myoglobin
GT Response
Red meat is divined as any meat that has myoglobin than white
Query: n/a
KnowledGPT
GT knowledge selected: False
Response: red meat is clussified as white meat. think it's interesting that only flesh from mammals or fowl is classified as red.
Query: protein powder
R2C2-PT
GT knowledge selected: False
Response: Athletes normally eat meat large quantitics.
Query: red meat protein
ELVM-3 0
GT knowledge selected: True
Response: In nutritional science, red meat defined any meat that has myoglobin than white
Query: red meat protein
ELVM w/ BM25
GT knowledge selected: Thue
Response: In nutritional science, red meat defined as meat that has more mynglobin than white meat.
Query: red meat protein GT knowledge selected: True
ELVM w/ DR
Response: Red meat is defined as meat that has more myoglohin than white meat.
Table Generated samples from WoW test unseen. We truncate ground-truth knowledge and display only relevant spans of the ground-truth knowledge to the ground-truth response.

---

Dialogue Context
When did Mark Spitz' swimming career end?
Following the Munich Olympics [..]
What else did Mark Spitz besides Speedo swimsuit after swimming career?
GT knowledge
[..] However, as the memory of his feats receded. so did his endorsement and promotional deals. As his endorsements faded, he started successful real-estate company in Beverly Hills. He bought Ferrari and says he made [...]
GT Response
Mark Spitz started successful real-estate company in Beverly Hills
Query: What did mark spitz do after his olympic career? GT knowledge selected: False
R2C2-PT
Response: 1999 Spitz ranked No. ESPN SportsCentury Greatest Athletes, only aquatic athlete make the list.
Query: Mark Spitz Speedo Swimsuit ads after speedo swimming career ends mark spitz Munich olympics 2012
ELVM-3
GT knowledge selected: False
Response: Spitz did Schick razor commercials and Speedo swimsuit ads
Query: Spitz endorsements after swimming career ends
ELVM w/ BM25
GT knowledge selected: True
Response: Mark Spitz started successful real-estate company in Beverly Hills.
Dialogue Context
What the New Scheme?
The 'new scheme" and the 'new dedication' respectively, initially scen as [...] [..]]
Did Ahmadi followers of the New Scheme act as missionaries?
part Mahmood Ahmad appointed men survey the Punjab [...]
Did Mirza Basheer-ud ud-Din Din Mahmood Ahmad missionaries try spread the New Scheme to foreign countries?
GT knowledge
[...] time scheme produced a vast amount literature in defence Islam in general and Ahmadiyya beliefs particular. The funds were also spent on the training and dispatching of Ahmadiyya missionaries outside Indian sub-continent and their sustenance. As part of this Mahmood Ahmad appointed [...]
GT Response
The funds were also spent the training and dispatching of Ahmadiyya missionarics outside the Indian sub-continent and their sustenance.
Query: Mirza Basher-ud Din Mahmood Ahmad missionaries GT knowledge selected: False
R2C2-PT
Response: Mirza Basheer-ud- Din Mahmood Ahmad used missionaries to try to spread the New Scheme to foreign countries.
Query: New Scheme Mahmud Ahmad missionaries spread Scheme in foreign countries
ELVM-3 0
GT knowledge selected: True
Response: Yes, have. have experienced heartache, also known as loss of a loved one.
Query: New scheme missionaries used spread Ahmadiyyah teachings in foreign countries
ELVM w/ BM25
GT knowledge selected: True
Response: The funds were also spent on training and dispatching Ahmmdiyyya missionaries outside the Indian sub-continent and their sustenance.
Table 23: Generated samples from QReCC test. We truncate the ground-truth knowledge and display only relevant spans of the ground-trum -ouuh knowledge to the ground-tren truth response.

---

Dialogue Context
How did Stephen Sondheim collaborate with James Lapine?
Stephen Sondheim found new collaborator in [...]
What did Stephen Sondheim and James Lapine do together in the 1980s?
Their first collaboration was Sunday in the Park with George (1984).
Was Stephen Sondheim and James Lapine's 's first collaboration a success?
Stephen Sondheim and James Lapine won the 1985 Pulitzer Prize [...]
Did Stephen Sondheim and James Lapine collaborate in other projects, besides Sunday in the Park with George?
[..]] last work together was the rhapsodic Passion (1994).
Was Stephen Sondheim and James Lapine's 's last work success?
GT knowledge
Sondheim and Lapine's s last work together was the rhapsodic Passion (1994), adapted from Ettore
Scola's 's Italian film Passione D' A'more. With a run of 280 performances, Passion was the shortest- ruaning show to win a Tony Award for Best Musical. [46) Later work Assassins opened [...]
GT Response
With of 280 performances, Passion was the shortest-running show win Tony Award for Best Musical.
Query: Was the play "rhapsodic passion" success or a failure?
R2C2-PT
GT knowledge selected: True
Response: Sondheim and Lapine's last work logether was the rhapsodic Passion (1994).
Query: Stephen Sonds and Lapine Rhapsodic passion 1994 Stephen sondheim, James lapine plays 12
ELVM-3 0
dreams off aff-broadway Pulitzer prize for play Sunday in Park.
GT knowledge selected: False
Response: Stephen Sondheim and James Lapine's s last work together was the rhapsodte Passion (1 (1994).
Query: Sondheim work was the Rhapsodic passion (1994) Pulitzer prize won Drama. Lapine
ELVM w/ BM25
wrote the play Twelve Dreams.
GT knowledge selected: True
Response: With run of 280 performances, Passion was shortest-running show to win a Tony
Award for Best Musical.
Dialogue Context
Did Frank Hague commit any questionable acts?
Yes, took kickbacks cash, relied on two ordinances of dubious [...]
Did Frank Hague do anything illegal?
passed an unconstitutional ordinance that gave himself as commissioner the power to [...]
Did anyone attempt to prosecute Frank Hague?
GT knowledge
[..]] Hague was able to stay in power despite nearly constant effort to turn him out of office from 1921 onward. [38] He was also able to avoid prosecution despite numerous federal and state investigations in part due to the fact he took most of his kickbacks in cash. [...]
GT Response
Yes, but he due the fact he took most of his kickbacks in cash.
also able avoid prosecution despite numerous federal and investigations in part
Query: Frank Hague prosecution
R2C2-PT
GT knowledge selected: False
Response: No.
tried to prosecute Frank Hague.
Query: Frank Hague ordinance ordinance frank Hague charges Frank Hague Hague for flouting constitutionality ordinances
ELVM-3 0
GT knowledge selected: False
Response: Hague was found guilty of contempt of court for ignoring the subpoena. He was fined $100 and stripped of his duties as Deputy Sheriff.
Query: Hague prosecution attempt failed after Hague passed unconstitutional ordinance. no Hague
ELVM w/ BM25
was prosecuted.
GT knowledge selected: True
Response: Hague was able to stay in power power nearly constant effort turn him out office from 1921 onward. He was also able avoid prosecution despite numerous federal and state investigations in part to the fact he most his kickbacks in cash.
Table 24:
Generated samples from QReCC test. We truncate the ground-truth knowledge and display only relevant spans of the ground-trun knowledge to the ground-trun truth response.

---

Human Evaluation Annotation Tool
Components of each example
Each may include
following
History: topic related to the conversation and the dialogue history, Bamplee may not contain 'Vstory'' Ieput text Utterance opponent
Ground
may reference decision.
Two esponses from different models.
Annotation Standards
given two difierent categories Fluency Relevance.
each
Flurney mexeures the cornectness, completeness, and samantic consisi consistancy of the resporse. mossures degree knowledge response history.
History:
Singing
love Some favorite genres sing blues, pop, and rock. like very much so.
Ray Charles Holiday.
Oh yeah love Billie Holiday, She had no formal music training but her vocal delivery made up for it.
Input Text:
I've been getting the strokes late. Do you like them?
Refivenc::
Strokes high school. debut Rolling Stones 502 greatest albums
time number
Response:
Yes I do like them. I like all kinds of music, but I'm not a big fan of the Strokes.
Model Response:
them. also like my Strokes shirt during because national stroke
Annotation
Fluency
Relevance
Both responses are fluent.
Both knowledges and responses relevant. Model response relevant.
Model A response
fluent.
Model 8 response is more
fluent.
Model response is more relevant.
Neither response fluent.
Neither relevant.
Previous
Next
Made with Streamiit
Figure 2: Screenshot of the annotation tool.


Reviews:
[{"review_id": "eaF8abHow3", "reviewer": "Reviewer_doRo", "summary": "", "questions": "+ Could you provide the upper bound performance figures for WoW and QReCC, assuming the golden knowledge is given?\n+ What is the rationale behind employing a singular model for both the query generator and response generator? What if their parameters were not shared?", "limitations": "", "rating": 2, "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": "We deeply appreciate the time and effort you have dedicated to reviewing our paper. Your insightful feedback has been instrumental in refining our work, and we are grateful for the constructive suggestions and the interest you've shown. We will incorporate your comments and feedback into our revised version. We have taken note of your concerns and questions and will address them in the sections below. \n\n**Validity of Equation 6:**\n    \nIt is possible that there is a bias that could be reduced using complex posterior modeling or posterior sampling (e.g., MCMC). Having said that, instead, for efficiency, we try to reduce the bias by sampling multiple queries. Namely, increasing the number of generated queries \\( m \\) aims to mitigate the bias caused by sampling from the prior. The results in Table 5 show improved performances as \\( m \\) increases, and a plateau upon reaching \\( m = 16 \\). We will apply and compare the use of posterior modeling and posterior sampling in the revised paper.\n\n**Posterior Distribution in Equation 9:**\n\nPlease note that the stop gradient is set on the posterior distribution, denoted as \\( q(u|x,y) \\) in Equation 9, which means that the common network trained by both equation 8 and 9 is only the prior network, and the prior network is updated to catch the posterior network in Equation 9. Since both loss functions are applied to update the same prior network, we did not encounter any instability or convergence issue during ELVM's training. \n\n**Use of R2C2 in ELVM's Training:**\n- **Addressing the Central Concern:**\n    \n    We deeply appreciate the thorough feedback regarding the possible attribution of ELVM's performance improvements to the utilization of R2C2-PT.\n    \n    To rigorously address the aforementioned concern, we have undertaken an additional experiment, deliberately omitting the post-pretraining stage. Our focus was to empirically validate whether the gains in performance arise primarily from the utilization of R2C2-PT or if they can be ascribed to the efficacy of our proposed algorithm, ELVM.\n    \n\n- **Experiment Design:**\n    \n    We start with a R2C2 transformer model as our base rather than R2C2-PT and then train with ELVM algorithm. The inference settings remained unaltered from Section 4.1 (line 422).\n    \n    To avoid ambiguity, we name this resulting model as “ELVM-from-R2C2” and rename the model trained from R2C2-PT as \"ELVM-from-R2C2-PT\", which corresponds to the \"ELVM\" terminology utilized in our original submission.\n\n- **Results:**\n    \n    Our results illustrate that while the standalone R2C2 model showed poor performances, our proposed ELVM leads to great improvements (ELVM-from-R2C2). Note that this improved performance is better than that of the previous SOTA model (SPI) and is comparable to the result obtained by ELVM-from-R2C2-PT. \n\n    | Model             | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n    |-------------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|:----:|\n    |                   |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n    | CoLV              |   39.6   |  -   |  2.9 | 20.6 |  7.9 |    54.3    |  -   |  2.1 | 19.7 |  6.3 |\n    | KnowledGPT        |   19.2   |  9.5 |  7.2 | 22.0 |  7.9 |    22.3    |  8.3 |  6.0 | 20.5 |  6.7 |\n    | SPI               |   17.1   | 10.2 |  7.7 | 22.7 |  8.8 |    19.1    |  9.6 |  7.3 | 22.0 |  8.5 |\n    | R2C2-PT           |   24.7   |  6.0 |  4.4 | 17.3 |  5.2 |    30.4    |  6.2 |  4.6 | 17.7 |  5.4 |\n    | ELVM-from-R2C2-PT |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n    | R2C2              |   40.7   |  1.7 |  0.9 | 12.3 |  1.9 |    46.6    |  1.6 |  0.9 | 11.9 |  5.4 |\n    | ELVM-from-R2C2    |   15.8   | 14.5 | 11.8 | 28.8 | 13.3 |    18.9    | 14.3 | 11.5 | 28.8 | 13.3 |\n\n- **Conclusions:**\n    \n    From the supplementary experiment, it is discernible that while R2C2-PT leads some advantages, the significant performance enhancements can be largely credited to our proposed algorithm, ELVM. This underscores ELVM's capability to drive performance improvements.\n\n**BART as Base:**\n\nWe acknowledge the valid point raised about the potential unfair advantage due to the discrepancy between our initial R2C2 model (400M parameters) and the BART-base model (140M parameters) used for the previous models including the previous SOTA model (SPI) on the WoW task.\n\n- **Addressing the Model Size Discrepancy:**\n    \n    To ensure a fair comparison and further demonstrate the robustness of our algorithm on diverse model scales, we conducted an experiment employing the BART-base as an initial model and then applied our proposed algorithm, ELVM, on the WoW dataset. The outcome of this training is termed “ELVM-from-BART.” Please note that due to the limted period and resource, we conduct this experiment on WoW dataset.\n\n- **Evaluation Results:**\n    \n    The initial BART model shows very poor performances. However, upon training the BART-base with our ELVM algorithm, there is a great improvement in performance across both WoW Seen and Unseen tasks. Importantly, the results surpass the performance benchmarks set by the previous SOTA model (SPI). This endorses the robustness and scalability of our algorithm, confirming its efficacy irrespective of the model size, from BART-base's scale (140M) to a billion-scale parameter size (as elaborated in Section 4.4, line 539). We believe this addresses the primary concern regarding the potential advantage due to model size and underscores the genuine contributions of our research.\n\n    | Model             | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n    |-------------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|:----:|\n    |                   |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n    | CoLV              |   39.6   |  -   |  2.9 | 20.6 |  7.9 |    54.3    |  -   |  2.1 | 19.7 |  6.3 |\n    | KnowledGPT        |   19.2   |  9.5 |  7.2 | 22.0 |  7.9 |    22.3    |  8.3 |  6.0 | 20.5 |  6.7 |\n    | SPI               |   17.1   | 10.2 |  7.7 | 22.7 |  8.8 |    19.1    |  9.6 |  7.3 | 22.0 |  8.5 |\n    | R2C2-PT           |   24.7   |  6.0 |  4.4 | 17.3 |  5.2 |    30.4    |  6.2 |  4.6 | 17.7 |  5.4 |\n    | ELVM-from-R2C2-PT |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n    | R2C2              |   40.7   |  1.7 |  0.9 | 12.3 |  1.9 |    46.6    |  1.6 |  0.9 | 11.9 |  5.4 |\n    | ELVM-from-R2C2    |   15.8   | 14.5 | 11.8 | 28.8 | 13.3 |    18.9    | 14.3 | 11.5 | 28.8 | 13.3 |\n    | BART              |   974.0  |  1.9 |  1.3 | 11.2 |  2.6 |    973.0   |  1.8 |  1.3 | 10.9 |  2.6 |\n    | ELVM-from-BART    |   15.3   | 10.6 |  8.4 | 25.8 | 11.1 |    16.4    | 10.4 |  8.2 | 25.7 | 10.9 |\n\n**Comparison with ZRKGC:** \n\nIn the revised version, we will incorporate the experiments for ZRKGC, complete with appropriate citations. For note, a pivotal distinction (Section 2.2) between our approach and ZRKGC is in the query generation methodology: our model explicitly generates search queries to retrieve relevant documents, while ZRKGC selects pertinent documents based on a similarity metric between the ground truth response and the documents. Thereby, the latent variable is search query for ELVM whereas it is document and knowledge grounding rate in ZRKGC. \n\nFurthermore, as discussed towards the end of Section 3.2, our proposed method differs from EM algorithm in that the expectation calculated using sampled from the prior instead of the posterior, through which the learning gradient flows hence training the prior accordingly. Moreover, there is a substantial performance discrepancy between our model and ZRKGC on the WoW test set. The performance metrics for the WoW Seen and Unseen tasks are delineated below:\n\n| Model | WoW Seen |      | WoW Unseen |      |\n|-------|:--------:|------|:----------:|------|\n|       |   PPL ↓  | F1 ↑ |    PPL ↓   | F1 ↑ |\n| ZRKGC |   40.4   | 18.7 |    41.5    | 18.6 |\n| ELVM  |   14.6   | 27.2 |    14.5    | 27.0 |\n\n---\n\n**Question 1. Upper Bound**\n    \nWe believe in the value of understanding the utmost capability in an ideal scenario. To this end, we pursued an upper-bound performance analysis for both the WoW and QReCC tasks. This approach involves training our response generator under the assumption that the model has access to the ground-truth document during both training and evaluation. The performance metrics of this specific setup are designated as \"ELVM-w-GT-Doc\" in the subsequent table.\n\n- WoW Result\n    | Model         | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n    |---------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|------|\n    |               |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n    | ELVM          |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n    | ELVM-w-GT-Doc |    9.7   | 22.1 | 18.5 | 40.9 | 23.9 |    10.5    | 21.5 | 17.9 | 40.7 | 23.3 |\n\n- QReCC Result\n    |     Model     |  F1↑  |  EM↑  |\n    |:-------------:|:----:|:----:|\n    | ELVM          | 36.5 |  6.2 |\n    | ELVM-w-GT-Doc | 55.4 | 16.2 |\n\nFrom the table, it's evident that there still remains a margin for improvement. We are committed to integrating this profound insight into our revision, as it not only showcases the potential of our approach but also underlines the extensive future work for more improvement.\n\n**Question 2. Parameter Share**\n    \nIn response to the question regarding parameter sharing in the training of ELVM, we conducted an additional experiment wherein we decoupled weights between the query generator and the response generator, leading to the creation of the \"EVLM-Decouple\" model. We evaluate the performance of EVLM-Decouple on both WoW test seen and unseen tasks and report in the table below.\n\n| Model         | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n|---------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|:----:|\n|               |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n| ELVM          |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n| ELVM-Decouple |   16.2   | 15.5 | 12.6 | 30.1 | 14.5 |    21.6    | 15.0 | 12.2 | 29.7 | 14.0 |\n\nUpon analysis of the results, we note a marginal performance improvement by EVLM-Decouple in comparison to ELVM. It is, however, essential to highlight the intrinsic advantage of the shared model approach, specifically in terms of computational resource efficiency and knowledge transfer during both the training and inference phases.\n\nWe acknowledge your feedback and will certainly incorporate this experimental result into the revised version of the paper.", "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 an innovative and efficient algorithm, ELVM, for knowledge-grounded dialogue generation.\n+ The proposed algorithm leverages a large amount of dialogue data and achieves state-of-the-art performances.", "reasons_to_reject": "+ The validity of the formula induction necessitates careful verification. Particularly, after the substitution of the posterior distribution $q(u \\mid x,y)$, does the inequality in Equation 6 still maintain its validity? Additionally, could the disparity in the definitions of the posterior distribution $q(u \\mid x,y)$ in Equations 8 and 9 potentially introduce convergence-related challenges?\n+ The potential for an unfair comparison in the experiments should be considered. The backbone of baseline methods such as KnowGPT or CoLV differ from the proposed method, which is established based on R2C2. Moreover, these baseline models encompass highly distinct parameter scales. Consequently, unless the authors adapt the previous methods to employ the same backbone (i.e., R2C2), the comparison could lack meaningful interpretation.\n+ A baseline method appears to be missing. ZRKGC[1] is a prominent technique in the context of knowledge-grounded dialogue, particularly in scenarios where knowledge annotations are unavailable.\n\n\n\n\n\n\n[1] Zero-Resource Knowledge Grounded Dialogue Generation", "questions_to_authors": "+ Could you provide the upper bound performance figures for WoW and QReCC, assuming the golden knowledge is given?\n+ What is the rationale behind employing a singular model for both the query generator and response generator? What if their parameters were not shared?", "paper_topic_and_main_contributions": "The paper proposes an efficient latent variable modeling algorithm, called ELVM, for knowledge-grounded dialogue generation. The algorithm leverages a large amount of dialogue data and enables joint training of the query generator and response generator without the need for annotated knowledge. The experimental results show that ELVM achieves state-of-the-art performances on diverse knowledge-grounded dialogue datasets.", "excitement": 2, "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 table 1, improper citation for SKT."}, "extra_text": {}}, {"review_id": "Sxxh0nnQ4Q", "reviewer": "Reviewer_unbb", "summary": "", "questions": "", "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": "We deeply appreciate the time and effort you have dedicated to reviewing our paper. Your insightful feedback has been instrumental in refining our work, and we are grateful for the constructive suggestions and the interest you've shown. We will incorporate your comments and feedback into our revised version. We have taken note of your concerns and questions and will address them in the sections below.\n\n**Datasets for R2C2 Training:**\n\nWe've detailed the datasets harnessed for training the R2C2 model in Table 10, Appendix A, aiming to instill crucial competencies for knowledge-grounded dialogue. Recognizing the significance of this information, we will move the table to the main section in the revised version. Respective descriptions pertaining to each dataset will be incorporated alongside.\n\n**Utilization of R2C2 in ELVM's Training:**\n\n- **Addressing the Central Concern:**\n    \n    We deeply appreciate the thorough feedback regarding the possible attribution of ELVM's performance improvements to the utilization of R2C2-PT.\n    \n    To rigorously address the aforementioned concern, we have undertaken an additional experiment, deliberately omitting the post-pretraining stage. Our focus was to empirically validate whether the gains in performance arise primarily from the utilization of R2C2-PT or if they can be ascribed to the efficacy of our proposed algorithm, ELVM.\n    \n\n- **Experiment Design:**\n    \n    We start with a R2C2 transformer model as our base rather than R2C2-PT and then train with ELVM algorithm. The inference settings remained unaltered from Section 4.1 (line 422).\n    \n    To avoid ambiguity, we name this resulting model as “ELVM-from-R2C2” and rename the model trained from R2C2-PT as \"ELVM-from-R2C2-PT\", which corresponds to the \"ELVM\" terminology utilized in our original submission.\n\n- **Results:**\n    \n    Our results illustrate that while the standalone R2C2 model showed poor performances, our proposed ELVM leads to great improvements (ELVM-from-R2C2). Note that this improved performance is better than that of the previous SOTA model (SPI) and is comparable to the result obtained by ELVM-from-R2C2-PT. \n\n    | Model             | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n    |-------------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|:----:|\n    |                   |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n    | CoLV              |   39.6   |  -   |  2.9 | 20.6 |  7.9 |    54.3    |  -   |  2.1 | 19.7 |  6.3 |\n    | KnowledGPT        |   19.2   |  9.5 |  7.2 | 22.0 |  7.9 |    22.3    |  8.3 |  6.0 | 20.5 |  6.7 |\n    | SPI               |   17.1   | 10.2 |  7.7 | 22.7 |  8.8 |    19.1    |  9.6 |  7.3 | 22.0 |  8.5 |\n    | R2C2-PT           |   24.7   |  6.0 |  4.4 | 17.3 |  5.2 |    30.4    |  6.2 |  4.6 | 17.7 |  5.4 |\n    | ELVM-from-R2C2-PT |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n    | R2C2              |   40.7   |  1.7 |  0.9 | 12.3 |  1.9 |    46.6    |  1.6 |  0.9 | 11.9 |  5.4 |\n    | ELVM-from-R2C2    |   15.8   | 14.5 | 11.8 | 28.8 | 13.3 |    18.9    | 14.3 | 11.5 | 28.8 | 13.3 |\n\n- **Conclusions:**\n    \n    From the supplementary experiment, it is discernible that while R2C2-PT leads some advantages, the significant performance enhancements can be largely credited to our proposed algorithm, ELVM. This underscores ELVM's capability to drive performance improvements.\n\n**BART as Base:**\n\nWe acknowledge the valid point raised about the potential unfair advantage due to the discrepancy between our initial R2C2 model (400M parameters) and the BART-base model (140M parameters) used for the previous models including the previous SOTA model (SPI) on the WoW task.\n\n- **Addressing the Model Size Discrepancy:**\n    \n    To ensure a fair comparison and further demonstrate the robustness of our algorithm on diverse model scales, we conducted an experiment employing the BART-base as an initial model and then applied our proposed algorithm, ELVM, on the WoW dataset. The outcome of this training is termed “ELVM-from-BART.” Please note that due to the limted period and resource, we conduct this experiment on WoW dataset.\n\n- **Evaluation Results:**\n    \n    The initial BART model shows very poor performances. However, upon training the BART-base with our ELVM algorithm, there is a great improvement in performance across both WoW Seen and Unseen tasks. Importantly, the results surpass the performance benchmarks set by the previous SOTA model (SPI). This endorses the robustness and scalability of our algorithm, confirming its efficacy irrespective of the model size, from BART-base's scale (140M) to a billion-scale parameter size (as elaborated in Section 4.4, line 539). We believe this addresses the primary concern regarding the potential advantage due to model size and underscores the genuine contributions of our research.\n\n    | Model             | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n    |-------------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|:----:|\n    |                   |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n    | CoLV              |   39.6   |  -   |  2.9 | 20.6 |  7.9 |    54.3    |  -   |  2.1 | 19.7 |  6.3 |\n    | KnowledGPT        |   19.2   |  9.5 |  7.2 | 22.0 |  7.9 |    22.3    |  8.3 |  6.0 | 20.5 |  6.7 |\n    | SPI               |   17.1   | 10.2 |  7.7 | 22.7 |  8.8 |    19.1    |  9.6 |  7.3 | 22.0 |  8.5 |\n    | R2C2-PT           |   24.7   |  6.0 |  4.4 | 17.3 |  5.2 |    30.4    |  6.2 |  4.6 | 17.7 |  5.4 |\n    | ELVM-from-R2C2-PT |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n    | R2C2              |   40.7   |  1.7 |  0.9 | 12.3 |  1.9 |    46.6    |  1.6 |  0.9 | 11.9 |  5.4 |\n    | ELVM-from-R2C2    |   15.8   | 14.5 | 11.8 | 28.8 | 13.3 |    18.9    | 14.3 | 11.5 | 28.8 | 13.3 |\n    | BART              |   974.0  |  1.9 |  1.3 | 11.2 |  2.6 |    973.0   |  1.8 |  1.3 | 10.9 |  2.6 |\n    | ELVM-from-BART    |   15.3   | 10.6 |  8.4 | 25.8 | 11.1 |    16.4    | 10.4 |  8.2 | 25.7 | 10.9 |\n\n**Distinctions:**\n\nVariational latent modeling to knowledge-intensive dialogue tasks is not novel and used in previous works. Thereby, we agree that the terminology ‘novel’ is ambiguous and will be removed in the revised version. Instead, we will draw more clear illustration of the distinction of our method from previous works that applies variational techniques to knowledge-intensive dialogue tasks in the revised paper.\n\nIn prior to the revision, we outline the distinctions between ELVM and [1] here. \n- First, the latent variables differ, as ELVM uses search queries as the latent variables, from which the relevant documents are retrieved using off-the-shelf retrievers, whereas [1] explicitly trains the retriever and treats the retrieved documents as the latent variable. \n- Second, ELVM is able to efficiently train the latent variable model by modifying the ELBO loss without extraneous encoder architectures or persistent update of document index. \n\nWe furthermore demonstrate the state-of-the-art performances in knowledge-intensive benchmarks (WoW and QReCC), showing outperformances over previous approaches by a significant margin.", "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 proposed algorithm achieves state-of-the-art performances without the use of the annotated knowledge, which ease the problem of expensive human annotation of knowledge information.\n2. This paper conducts several experiments on benchmark datasets and analyzes the experimental results comprehensively.\n", "reasons_to_reject": "1. In line 391, you fine-tune R2C2 pre-trained transformer model on multiple datasets to acquire the essential skill sets for knowledge-grounded dialogue such as query generation and knowledge-grounded response generation. It would be better if you could supplement what the datasets is in detail to judge if it may result in unfair experimental settings between baselines and the proposed model.\n2. In the baseline of KnowledGPT, the authors employ BERT and GPT-2 for knowledge selection and dialogue generation, respectively, which is different from the model scale/pretraining corpus size employed in this paper. So I have concerns regarding whether the performance improvements in this work are attributed to the utilization of a more proficient pre-training language model.\n3. Utilizing variational techniques to enhance knowledge-intensive text generation tasks is not a novel approach. Besides, it is suggested that the authors provide more illustration on the distinctions between proposed method and other similar approaches in this field (e.g. [1])\n\n[1] End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering. In NeurIPS 2021.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper proposes an efficient latent variable modeling for knowledge-grounded dialogue generation where the query generator for knowledge retrieval from the off-the-shelf retriever and the dialogue response generator grounded on the retrieved knowledge are simultaneously trained without the knowledge supervision. The proposed ELVM demonstrates its effectiveness in performing unsupervised joint training and significant performance improvements over previous methods.", "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": {"Missing_References": "End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering. In NeurIPS 2021."}, "extra_text": {}}, {"review_id": "4DTPR8DeYW", "reviewer": "Reviewer_ivGY", "summary": "", "questions": "1. I notice that the evaluation does not include the reporting of the Knowledge F1 (KF1) metric. Could you please explain the reason behind this decision? Also, the human evaluation did not include the R2C2-PT baseline. Can you provide some clarification on this matter?\n\n2. I notice that your method suggests fixing the knowledge retriever during training. However, it seems that if we switch to a new knowledge retriever, the model will need to be re-trained. Can you please explain how this approach contributes to the overall efficiency of the model?", "limitations": "", "rating": 3, "confidence": 2, "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 deeply appreciate the time and effort you have dedicated to reviewing our paper. Your insightful feedback has been instrumental in refining our work, and we are grateful for the constructive suggestions and the interest you've shown. We will incorporate your comments and feedback into our revised version. We have taken note of your concerns and questions and will address them in the sections below.\n\n**On Performance Improvement:**\n\nWe respectfully differ with the view that our improvement is marginal. Analyzing the metrics reveals a clear trend of significant improvement. For the WoW unseen task, ELVM attains 14.5 (Bleu-3) and 29.2 (Rouge-1). This contrasts with SPI's 9.6 and 22.0 and KnowledGPT's 8.3 and 6.0 for the same metrics. Clearly, the gap between ELVM and SPI is larger than the difference between SPI and KnowledGPT. \n\nIn the QReCC evaluation, our model registers 36.5 on the F1 metric, notably outperforming previous models like QKConv (33.5) and DPR-IHN (30.4). Moreoever, augmenting our model with a dense retriever during inference, akin to the methodology of QKConv and DPR-IHN, enhances our F1 score to 37.9.\n\n**On Human Evaluation:**\n\nWe intended to undertake human evaluations compared to robust models like SPI. However, the unavailability of SPI's checkpoint led us to opt for KnowledGPT, a commendable performer amongst accessible models. Given the considerable expenditure linked with human evaluations, we could not include R2C2-PT in our human evaluation. Another reason for the exclusion of R2C2-PT is in the findings of Table 1, where R2C2-PT underperforms compared to KnowledGPT. \n\n----\n\n**Question 1. Metrics Reporting:**\n\nThank you for highlighting the importance of comprehensive metric representation in our study. We concur with your view that including metrics like Knowledge-F1 (KF1) can advance thoroughness and comprehensiveness of our analysis. In response to the feedback, we report both F1 and KF1 metrics for our models and the values of previous models are drawn from [1]. As similar to other metrics, ELVM shows better performances than the previous methods in terms of both F1 and KF1. We will ensure a comprehensive metric inclusion in the revised version.\n\n| Model        | WoW Seen |      | WoW Unseen |      |\n|--------------|:--------:|------|------------|------|\n|              |    F1↑   |  KF1 |     F1↑    |  KF1↑ |\n| CoLV         |   20.3   | 18.2 |    18.5    | 17.5 |\n| KnowledGPT   |   22.0   | 23.8 |    20.5    | 22.1 |\n| SPI          |     -    |   -  |      -     |   -  |\n| R2C2-PT      |   15.4   | 20.5 |    15.7    | 22.3 |\n| ELVM-OK      |   23.0   | 33.0 |    22.9    | 33.0 |\n| ELVM-EM-Like |   24.5   | 33.2 |    23.5    | 33.5 |\n| ELVM         |   27.2   | 40.8 |    27.0    | 40.3 |\n\n**Question 2. Retriever Switching:**\n\nTo assess the robustness of our algorithm to retriever mismatch between training and inference phases, we undertake an additional experiment, of which the results are depicted in the table below. In this experiment, we train the WoW task utilizing eitherBM25 or dense retriever (utilizing the all-MiniLM-L6-v2 encoder, as detailed in line 528), and subsequently evaluate using eitherBM25 or dense retriever. The results on the WoW unseen test indicate that the use of a different retriever at the test time does not require re-training of the model, considering just the better performance by dense retriever than BM25 in general.\n\n| Model      | Train Retriever | Inference Retriever | PPL ↓ | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ | F1↑   | KF1↑  |\n|------------|-----------------|---------------------|-------|------|------|------|------|------|------|\n| CoLV       | -               | -                   | 54.3  | -    | 2.1  | 19.7 | 6.3  | 18.5 | 17.5 |\n| KnowledGPT | -               | -                   | 22.3  | 8.3  | 6.0  | 20.5 | 6.7  | 20.5 | 22.1 |\n| SPI        | -               | -                   | 19.1  | 9.6  | 7.3  | 22.0 | 8.5  | -    | -    |\n| ELVM       | BM25            | BM25                | 18.0  | 14.5 | 11.8 | 29.2 | 13.7 | 27.0 | 40.3 |\n| ELVM       | BM25            | Dense               | 14.8  | 15.5 | 12.6 | 31.2 | 15.2 | 29.3 | 45.4 |\n| ELVM       | Dense           | Dense               | 12.9  | 15.2 | 12.8 | 31.1 | 15.5 | 29.0 | 45.6 |\n| ELVM       | Dense           | BM25                | 21.6  | 14.5 | 11.8 | 28.4 | 13.1 | 28.2 | 44.4 |\n\n[1] Generative Knowledge Selection for Knowledge-Grounded Dialogues. Sun et al.", "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 proposed Latent Variable Model (ELVM) introduces a significant innovation in the field of knowledge-grounded dialogue generation. Unlike traditional models, the ELVM does not require annotated knowledge or explicit training of knowledge retrieval. This feature simplifies the training process and makes the model more adaptable to various applications.\n\n2. The training objective eliminates the need for extra modeling of a surrogate posterior distribution or online posterior inference. This simplifies the training process and improves its effectiveness.", "reasons_to_reject": "1. While the paper introduces a novel approach in the form of the ELVM, the actual improvement over existing methods appears to be marginal.\n\n2. The paper falls short in thoroughly analyzing the knowledge grounding performance. Key metrics such as Knowledge F1 are not evaluated.\n\n3. It seems that the paper's human evaluation part is lacking as it does not cover all pertinent baselines for comparison.", "questions_to_authors": "1. I notice that the evaluation does not include the reporting of the Knowledge F1 (KF1) metric. Could you please explain the reason behind this decision? Also, the human evaluation did not include the R2C2-PT baseline. Can you provide some clarification on this matter?\n\n2. I notice that your method suggests fixing the knowledge retriever during training. However, it seems that if we switch to a new knowledge retriever, the model will need to be re-trained. Can you please explain how this approach contributes to the overall efficiency of the model?", "paper_topic_and_main_contributions": "This paper introduces an approach to knowledge-grounded dialogue generation through the development of an Efficient Latent Variable Modeling (ELVM). The model incorporates a query generator, which is then followed by an off-the-shelf retriever that is fixed during the training process. The key innovation of the ELVM is its ability to conduct efficient and robust training without the need for annotated knowledge or explicit training of knowledge retrieval. Experimental results demonstrate the effectiveness of this approach.", "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": {"Missing_References": "KGPT: Knowledge-Grounded Pre-Training for Data-to-Text Generation - Chen et al.\n\nKnowledge-grounded dialogue generation with a unified knowledge representation - Li et al.\n\nLow-resource knowledge-grounded dialogue generation - Zhao et al."}}, {"review_id": "p76qnwWvLG", "reviewer": "Reviewer_UuJj", "summary": "", "questions": "Question A:\n\nWhy the Equation 3 (the conditional likelihood of response) not consider the term of $P(Z(u) | u)$?\n\nQuestion B:\n\nOn the QReCC dataset, the response generator takes **TEN** knowledge to predict the response. However, the knowledge retrieval Recall@10 of dialogue history surpasses ELVM in Table 13. What is the effect of training the response generator directly with the dialogue history as the query?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We deeply appreciate the time and effort you have dedicated to reviewing our paper. Your insightful feedback has been instrumental in refining our work, and we are grateful for the constructive suggestions and the interest you've shown. We will incorporate your comments and feedback into our revised version. We have taken note of your concerns and questions and will address them in the sections below.\n\n**The contribution is unclear:**\n\nWe acknowledge the potential confusion arising from the phrasing in our paper. In light of your feedback, we will have removed the term \"novel\" from line 143 in our revised version, and restate our first contribution as: \"An efficient latent variable modeling is proposed in joint training of query generator and dialogue response generator without the knowledge supervision for knowledge-intensive dialogue generation”.  We hope these adjustments better convey our intentions and address your concerns.\n\n**Use of R2C2 in ELVM's Training:**\n\n- **Addressing the Central Concern:**\n    \n    We deeply appreciate the thorough feedback regarding the possible attribution of ELVM's performance improvements to the utilization of R2C2-PT.\n    \n    To rigorously address the aforementioned concern, we have undertaken an additional experiment, deliberately omitting the post-pretraining stage. Our focus was to empirically validate whether the gains in performance arise primarily from the utilization of R2C2-PT or if they can be ascribed to the efficacy of our proposed algorithm, ELVM.\n    \n\n- **Experiment Design:**\n    \n    We start with a R2C2 transformer model as our base rather than R2C2-PT and then train with ELVM algorithm. The inference settings remained unaltered from Section 4.1 (line 422).\n    \n    To avoid ambiguity, we name this resulting model as “ELVM-from-R2C2” and rename the model trained from R2C2-PT as \"ELVM-from-R2C2-PT\", which corresponds to the \"ELVM\" terminology utilized in our original submission.\n\n- **Results:**\n    \n    Our results illustrate that while the standalone R2C2 model showed poor performances, our proposed ELVM leads to great improvements (ELVM-from-R2C2). Note that this improved performance is better than that of the previous SOTA model (SPI) and is comparable to the result obtained by ELVM-from-R2C2-PT. \n\n    | Model             | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n    |-------------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|:----:|\n    |                   |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n    | CoLV              |   39.6   |  -   |  2.9 | 20.6 |  7.9 |    54.3    |  -   |  2.1 | 19.7 |  6.3 |\n    | KnowledGPT        |   19.2   |  9.5 |  7.2 | 22.0 |  7.9 |    22.3    |  8.3 |  6.0 | 20.5 |  6.7 |\n    | SPI               |   17.1   | 10.2 |  7.7 | 22.7 |  8.8 |    19.1    |  9.6 |  7.3 | 22.0 |  8.5 |\n    | R2C2-PT           |   24.7   |  6.0 |  4.4 | 17.3 |  5.2 |    30.4    |  6.2 |  4.6 | 17.7 |  5.4 |\n    | ELVM-from-R2C2-PT |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n    | R2C2              |   40.7   |  1.7 |  0.9 | 12.3 |  1.9 |    46.6    |  1.6 |  0.9 | 11.9 |  5.4 |\n    | ELVM-from-R2C2    |   15.8   | 14.5 | 11.8 | 28.8 | 13.3 |    18.9    | 14.3 | 11.5 | 28.8 | 13.3 |\n\n- **Conclusions:**\n    \n    From the supplementary experiment, it is discernible that while R2C2-PT leads some advantages, the significant performance enhancements can be largely credited to our proposed algorithm, ELVM. This underscores ELVM's capability to drive performance improvements.\n\n**BART as Base:**\n\nWe acknowledge the valid point raised about the potential unfair advantage due to the discrepancy between our initial R2C2 model (400M parameters) and the BART-base model (140M parameters) used for the previous models including the previous SOTA model (SPI) on the WoW task.\n\n- **Addressing the Model Size Discrepancy:**\n    \n    To ensure a fair comparison and further demonstrate the robustness of our algorithm on diverse model scales, we conducted an experiment employing the BART-base as an initial model and then applied our proposed algorithm, ELVM, on the WoW dataset. The outcome of this training is termed “ELVM-from-BART.” Please note that due to the limted period and resource, we conduct this experiment on WoW dataset.\n\n- **Evaluation Results:**\n    \n    The initial BART model shows very poor performances. However, upon training the BART-base with our ELVM algorithm, there is a great improvement in performance across both WoW Seen and Unseen tasks. Importantly, the results surpass the performance benchmarks set by the previous SOTA model (SPI). This endorses the robustness and scalability of our algorithm, confirming its efficacy irrespective of the model size, from BART-base's scale (140M) to a billion-scale parameter size (as elaborated in Section 4.4, line 539). We believe this addresses the primary concern regarding the potential advantage due to model size and underscores the genuine contributions of our research.\n\n    | Model             | WoW Seen |      |      |      |      | WoW Unseen |      |      |      |      |\n    |-------------------|:--------:|:----:|:----:|:----:|:----:|:----------:|:----:|:----:|:----:|:----:|\n    |                   |   PPL ↓  | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |    PPL ↓   | B3 ↑ | B4 ↑ | R1 ↑ | R2 ↑ |\n    | CoLV              |   39.6   |  -   |  2.9 | 20.6 |  7.9 |    54.3    |  -   |  2.1 | 19.7 |  6.3 |\n    | KnowledGPT        |   19.2   |  9.5 |  7.2 | 22.0 |  7.9 |    22.3    |  8.3 |  6.0 | 20.5 |  6.7 |\n    | SPI               |   17.1   | 10.2 |  7.7 | 22.7 |  8.8 |    19.1    |  9.6 |  7.3 | 22.0 |  8.5 |\n    | R2C2-PT           |   24.7   |  6.0 |  4.4 | 17.3 |  5.2 |    30.4    |  6.2 |  4.6 | 17.7 |  5.4 |\n    | ELVM-from-R2C2-PT |   14.6   | 14.7 | 11.9 | 29.3 | 13.9 |    18.0    | 14.5 | 11.8 | 29.2 | 13.7 |\n    | R2C2              |   40.7   |  1.7 |  0.9 | 12.3 |  1.9 |    46.6    |  1.6 |  0.9 | 11.9 |  5.4 |\n    | ELVM-from-R2C2    |   15.8   | 14.5 | 11.8 | 28.8 | 13.3 |    18.9    | 14.3 | 11.5 | 28.8 | 13.3 |\n    | BART              |   974.0  |  1.9 |  1.3 | 11.2 |  2.6 |    973.0   |  1.8 |  1.3 | 10.9 |  2.6 |\n    | ELVM-from-BART    |   15.3   | 10.6 |  8.4 | 25.8 | 11.1 |    16.4    | 10.4 |  8.2 | 25.7 | 10.9 |\n\n---\n\n**Question A. $P(Z(u)|u)$:**\n\nWe appreciate your observation regarding Equation 3. Indeed, we did not incorporate the term $P(Z(u)|u)$ because it represents a deterministic mapping, analogous to a delta function. As we employ a static retriever such as BM25, rather than joint training of a document retriever, we can safely make the assumption that $P(Z(u)|u)$ equals to one.\n\n**Question B. Dialogue History as Query:**\n\nAs an additional experiment, we directly train the dialogue response generator by treating the dialogue history itself as a query. During evaluation, this dialogue history is also used as the query to retrieve the top-\\{ 1, 5, 10 \\} most relevant documents ($k$) via the BM25 retriever. We designate this model \"ELVM-Context-as-Query\" and compare its performance against ELVM.\n\nIn the following table, we report three values for each metric corresponding to using top-\\{ 1, 5, 10 \\} documents from the retriever. We can observe a significant gap between ELVM and ELVM-Context-as-Query. In other words, although using the dialogue context as a query achieves marginally better document recall than using ELVM’s generated query at $k=10$ as in Table 13, the final performance of ELVM is better than that of ELVM-Context-as-Query.\n\n| Model                 | F1↑   |      |      | EM↑  |     |      |\n|-----------------------|------|------|------|-----|-----|------|\n|                       | $k$=1  | $k$=5  | $k$=10 | $k$=1 | $k$=5 | $k$=10 |\n| QKConv                | -    | -    | 33.5 | -   | -   | 5.9  |\n| ELVM-Context-as-Query | 27.5 | 33.1 | 35.6 | 2.6 | 4.7 | 4.8  |\n| ELVM                  | 29.1 | 35.0 | 36.5 | 3.1 | 5.4 | 6.2  |\n\nIn addition, we try to identify the reason of why the dialogue context can be a strong query on the QReCC task, especially when the number of retrieved documents is large. We observe that this is due to the substantial alignment between the dialogue context and the ground-truth knowledge especially in the QReCC test dataset, in contrast to the WoW test dataset. Our obtained F1 scores between the dialogue context and ground-truth knowledge support this: QReCC shows a score of 21.1, which is significantly higher than those of the WoW test seen (10.7) and unseen (11.3) tasks.", "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 motivation of this paper is intuitive and easy to understand.\n2. The proposed method is clearly clarified and makes a step in modifying the ELBO.\n3. The automatic and human evaluation shows the proposed method achieves state-of-the-art results on two different datasets.", "reasons_to_reject": "1. The contribution is unclear.\n\nFor example, in Line 143, this paper states the first contribution is a novel latent variable modeling method that trains query and response generators simultaneously via joint training. However, this contribution is ambiguous, as previous research has already proposed queries as intermedia results and joint training of query and response generators, as stated in Line 252.\n\n2. The effectiveness of the proposed method without a pretraining process is unclear. \n\nELVM is initial from R2C2-PT, which is post-pretrained in many other knowledge-grounded conversation datasets. However, the baseline models compared in this paper are trained from vanilla pretrained models. This paper does not validate the method's effectiveness without post-pretraining, or compare baseline methods on R2C2-PT.", "questions_to_authors": "Question A:\n\nWhy the Equation 3 (the conditional likelihood of response) not consider the term of $P(Z(u) | u)$?\n\nQuestion B:\n\nOn the QReCC dataset, the response generator takes **TEN** knowledge to predict the response. However, the knowledge retrieval Recall@10 of dialogue history surpasses ELVM in Table 13. What is the effect of training the response generator directly with the dialogue history as the query?", "paper_topic_and_main_contributions": "This paper focuses on the problem setting of knowledge-grounded dialogue generation. Prior works with finetuning knowledge retrievers have constraints on the use of complex and large-scale retrievers, or train-infer discrepancy when involving posterior probability. To alleviate these limitations, this paper proposes a latent variable modeling (namely ELVM) with a query generator, an off-the-shelf knowledge retriever, and a response generator, which treats queries instead of retrieved knowledge as a latent variable and applies a modified ELBO. The proposed method achieves SOTA on two datasets.", "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": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper introduces a new approach to knowledge-grounded dialogue generation, termed Efficient Latent Variable Modeling (ELVM). ELVM incorporates a query generator, followed by an off-the-shelf retriever, which remains fixed during the training process. Notably, ELVM aims to facilitate efficient and robust training without the requirement for annotated knowledge or explicit training of knowledge retrieval.

While the utilization of variational techniques to enhance knowledge-intensive text generation tasks is not new, ELVM demonstrates its effectiveness in unsupervised joint training, leading to substantial performance enhancements compared to prior methods. However, despite the novelty of ELVM, the actual improvements over existing methods may appear relatively modest.
Decision: Accept-Findings