Exploring In-Context Learning for Knowledge Grounded Dialog Generation
Authors: Qinyu Chen
Wenhao Wu
Sujian Li
Venue: EMNLP 2023 Findings (2023)
Keywords: dialog
knowledge
large language models
in-context learning
retrieval system

Abstract: Large neural-based  dialog generation  models have been applied in many real-life scenarios, yet they are prone to hallucination and tend to produce factually inaccurate outputs which raise great concerns. 
To alleviate this problem, we propose a plug-and-play retrieval-based framework IKA, which leverages  in-context learning  and retrieval techniques to enhance LLMs on knowledge grounded dialog generation.
We design thorough experiments on a large-scale knowledge graph with 1M+ facts to investigate the effectiveness and generalization of our framework. 
Experiments show that our method surpasses previous training-based SOTA by a large margin, specifically 46.67% in BLEU4, 26.01% in ROUGE-L, 122.90% in BARTScore and 30.50% in Entity Coverage F1.
Further analysis show promising abilities of LLMs to perform knowledge-intensive tasks, which is previously considered weak and understudied.

Exploring In-Context Learning for Knowledge Grounded Dialog Generation
Qinyu Chen, Wenhao Wu and Sujian Li
School of Computer Science, Peking University
National Key Laboratory for Multimedia Information Processing, Peking University (chenqinyu, 1isujiinnnnnnn edu. cn
Abstract
Large neural-based dialog generation models have been applied in many real-life scenarios, yet they are prone to hallucination and tend to produce factually inaccurate outputs which raise great concerns. To alleviate this problem, we propose plug-and-play retrieval-based framework IKA, which leverages in-context learning and retrieval techniques enhance LLMs on knowledge grounded dialog gener- ation. We design thorough experiments on large-scale knowledge graph with 1M+ facts (Moon et al., 2019) to investigate the effec- tiveness and generalization of our framework. Experiments show that our method surpasses previous training-based SOTA by a large mar- gin, specifically 46.67 % in BLEUU, 26.01 % in ROUGE-L, 122.90% % in BARTScore and Further analy-
you information Harco Reus?
player
Gezmany mational.
fostball tean. is nidtieldez tzon west Gezmany. Do you like fostball?
Ylor Aiita
Germany natioual
foetball
follow it around world cup cine, is Toni Kroos also on the Geznan tean?
County of
East Germany
No! Toni Keoos is also gealkeeper on the Geranny national foothali
Figure I: An example conversation wherein the agent receives correct knowledge but still generates halluci- nated response. Toni Kroos is the midfielder rather than the goalkeeper of Germany national football team, which contradicts the knowledge paths provided.
30.50% in Entity Coverage sis shows promising abilities of LLMs to per- form knowledge-intensse tasks, which is pre- viously considered weak and understudied.
1 Introduction
compensate for the lack of relevant knowledge in the generative model, which usually inefficient in training and unsatisfactory in performance.
Existing knowledge grounded dialogue (KGD) sys- tems aim to make use of external knowledge to produce reasonable dialogue responses (Ji et al., 2022; Dziri et al., 2021; Zhang et al., 2019; He et al., 2017). To introduce the KGD task, Figure gives example; the left side of the figure pro- vides the corresponding knowledge graph, while the right side of the figure shows the dialogue his- tory and the response. The goal KGD to gen- erate fluent and faithful response based on context and knowledge.
Recently, large language models (Brown et al.. 2020; OpenAI, 2023) have been applied to va- riety of tasks. They store rich parametric world knowledge through pre-training and are able to pro- duce fluent and human-like responses. However, they uncontrollably generate content that is incon- sistent with factual knowledge and even contain hallucinations (Shuster et al., 2021; Dziri et al., 2022b,a; Mielke et al., 2022). Thus, hallucination in large language models (Ji et al., 2023) is a big concern in real-world applications, as a growing number of users directly integrate LLMs into their daily pipelines without realizing this risk (Tiku, 2022). We also use GPT3.5 (er335(text-daainci-00) conduct preliminary experiments on KGD, where the context and knowledge are concatenated as tex- tual input to generate response, and find similar
The earlier works of KGD focus on the use of knowledge graphs, where sub-graphs are retrieved using entities and relationships given dialog history and relevant knowledge. Generally, they utilize a separate knowledge encoder module. Errors in knowledge retrieval stage may propagate to the generative model (Sarkar et al., 2022; Ji et al., 2022; Kang et al., 2022). This type of methods aims to

---

hallucination problem. We can see the ability to leverage knowledge in LLMs is non-trivial. How to leverage knowledge the focus of our research. Large language models like GPT-3 (Brown et al., 2020) and LaMDA (Thoppilan et al., 2022) have demonstrated emergent in-context learning abili- ties, which becomes a new research paradigm for LLMs. In this paradigm, task definition, demonstra- tion examples and target context are concatenated as the input prompt for LLMs. The example selec- tion process is vital to the effectiveness of ICL (Min et al., 2022) We believe a more refined example retrieval stage based on both dialogue history and knowledge is needed. To the best of our knowledge, no previous work (Ji et al., 2022; Kang et al., 2022; Dziri et al., 2021) has explored the potential of ICL in knowledge grounded dialogue generation.
task.
We give comprehensive empirical study on many aspects of ICL strategies in KGD, including the choice of retrieval methods, knowledge representation etc. These findings are valuable for a deeper understanding LLMs.
We propose a plug-and-play retrieval based framework called (KA(In-co Knowledge grounded dialog Augmenter). IKA is applica- ble across LLMs and significantly surpasses previous works (Ji et al., 2022; Sarkar et al.. 2022; Dziri et al., 2021).
Related Works
2.1 Hallucination in Open-domain Dialogue Generation
In order to overcome these issues, we propose IKA framework as an effective plug-and-play so- lution. The principle of IKA is to retrieve high quality in-context demonstrations to guide LLMs. A high-level overview is illustrated in Figure 2. IKA consists of three modules, a retrieval module for retrieving relevant examples based history and knowledge, DER module to improve the di- versity of retrieved examples and a prompt con- structing module to build the final input for LLMs. By plug-and-play means no additional training or parameter updates are required. The retrieval mod- ule can be implemented by any retrieval methods, and the inference LLMs can be either open spercsource black-box.
How reduce the false information in generated text is a popular research lopic in natural language generation. Previous works have studied the hal- lucination in sub-field of NLG such as abstrac- tive summarization or machine translation (Huang et al., 2021; Lee et al., 2018). Dialogue gener- ation is different from above tasks in that it re- quires multi-tum, long-term interaction. Dziri et al. (2022b) propose to split hallucination in open- domain dialogue into intrinsic and extrinsic hal- lucination. Some methods propose to improve the transformer (Vaswani et al., 2017) architecture increase dialog faithfulness. Xu et al. (2021) lever- age light-weight adapter as knowledge expert to enhance GPT-2 (Radford et al., 2019). Wu et al. (2021) apply an inductive attention mechanism for self-attention-basse generation models. Instead of improving the generation model itself, Dziri et al. (2021); Ji et al. (2022); Sarkar et al. (2022) pro- pose add extra module such as knowledge graph grounding module or re-ranking module to reduce hallucination.
Experimental results show that our method sig- nificantly outperforms state-of-the-ar art (Ji et al., 2022) on OpendialKG(Moon
al., 2019). In automated evaluation, our framework able to gain 46.67% % on BLEU4, 26.01 % on ROUGE- L. 122.90% % on BARTScore and 30.50% on En- tity Coverage FI. For the choice of retrieval method, our best results have 18.43%, 27.75% and 69.78% % relative gains BLUE, ROUGE-L and BARTScore (Yuan al., 2021) compared to random retrieval baseline. Our proposed diversify strategy achieves the highest faithfulness evaluation results. The quantitative and qualitative investiga- tion further demonstrates its efficacy. In summary, our contributions are as follows:
2.2 In-context Learning
Large language models pre-trained on next-token prediction objective have been found to have the ability to adapt to new task with only few exam- ples input (Brown al., 2020), which means they can be applied to different tasks at inference time without expensive training. However, the the- ory and mechanism of in-context learning have not been well studied. Some researchers (Wang et al., 2023) argue that in-context learning happens when
We are the first apply in-context learning to knowledge grounded dialog generation. Abla- tion experiments demonstrate the outstanding performance of ICL for snowledge-intensive intensive

---

Dinlog History
Knowlcdge
Instruction
Sparse Retrieval
Target Context
Response
Dense Retrieval
directed the movie 308: Rise of an Empire. Is
anything in particular you' like to know about him?
Hybrid Retrieval
Reference Dataset
Retrieval Module
That's great!
an Empire 1s a great movie. you any movies directed by Noam Murro?
Prompt
lI
Diverse
Yes, he directed 380: Rise of Empire.
Example Retrieval (optional)
GPT-J
ICL Examples
Target Context
LLMs
Generated Responses
Figure 2: An overview of IKA framework.
a latent concept is inferred at test time. Olsson et al. (2022) contribute in-context learning ability of transformer- baasd model the existence of "in- duction heads". Akyürek al. (2022) further verify this hypothesis on toy linear regression. The ex- ample selection process has also become a popular research topic. Liu et al. (2021) propose to retrieve in-context examples using nearest neighbor algo- rithm. In supervised learning, the input-label corre- spondence is very important, but Min et al. (2022) argue that ground truth labels are not required in ICI. which is quite counter-intuitive.
response is complex even for powerful LLMs. Re- cent work (Evanson et al., 2023) points out the sim- ilarity between training large language models and children's language acquisition, therefore we argue that similar mechanism may exist in prompting lan- guage models to perform specific task. An analogy is instructing a high school student to pass open- book exam. A smart teaching technique would be to provide some well selected input-output exam- ples so that the student understands how to use given knowledge to produce high quality answers. first give formal descrip-
In this section, tion of KGD task and introduce current research challenges. Then, we formulate KGD task under a conditional text generation framework using in- context learning techniques. In addition to tradi- tional in-context examples selection strategies, we design several retrieval strategies to ground our se- lection process on dialogue history and relevant knowledge.
3 Methodology
We propose a slug-and-play retrieval based frame- work called IKA(In-context Knowledge grounded dialog Augmenter) to enhance LLMs' faithful gen- eration ability. The overview of our method is presented in Figure 2. We conduct broad experi- ments mainstream LLMS such as GPT-2 (Rad- ford et al., 2019), GPT-J (Wang and Komatsuzaki,, 2021), GPT-3 (Brown et al., 2020), GPT-4 (Ope- nAI, 2023) and Falcon (Almazrouei et al., 2023). Most of them are general purpose LLMs pre- trained on large-scale corpus and have not been fine-tuned or optimized for KGD task.
3.1 Task Definition
For dialogue systems, generating responses in- volves handling data samples that include a dia- logue history H, a collection of utterances U that reflect interaction between human users and AI assistants.
We find KGD especially suitable for in-context learning mainly for two reasons:
Here focus knowledge grounded dialog task, which is an extended setting of dialogue re- sponse generation. In KGD task, the conversation system is typically equipped with a knowledge base that contains a wide range information in the
(1). the provided knowledge is very helpful in selecting semanti- cally similar demonstrations (2). how to lever- age given knowledge and generate high quality

---

form of text or triplets. Factual triplets are often presented in the form of (subject, predicate, ob- ject). The subject and object are entities in the knowledge graph, and predicate represents the rela- tionship between the two. The goal of KGD is to generate faithful and informative response based on dialog history H and knowledge K. We have faith- fully followed the setup of previous works (Zhou al., 2021; Dziri al., 2021; Ji et al., 2022).
Instruction: Generate shor response based on conversation
history and knowledge.
Ins truction
Here exomples:
Prompt Instruction Examples Target
History:
User Could you recommmend movies staring Nouh Emmerich?
Aseistant: Sare, check Windtalkess and Tumbleweeds.
Windtalkers?
Knowledge;
Roger Willie, Laclors, Windtalksers
ICL Examp les
Response:
Roger Willie also Windtalkers
3.2 Model KGD as Conditional Text Generation via In-Context Learining
History:
User: you know kind music Luke Beyan performs Assistant I like singing along to his song Do n't Want This Night to End. How about you
We propose model KGD as conditional text generation (Hu and Li, 2021) problem. The output sequences are conditioned on the combination of instruction, in-context examples, dialog history and knowledge. To the best of our knowledge, we are the first to model KGD under the framework of in-context learning. Concretely, the probability of generating target y given input x can be modeled as:
Knowledge:
Don't Want This Night to End, Composer, Luke Bryan Bryun, Composer,, Response:
Trucks Target Context
Figure 3: An example of prompt
we propose to model the dependency between ex- amples, which can be expressed as:
15 (LM() II PLM K,H,KKK.ei (1) 1=
K) = II I.,.CHH,,KC&.). (3) ==1
where LM denotes the parameters of language mod- els, H denotes dialog history and K denotes knowl- edge. In the setting of in-context learning, exam- ples are required instruct LLMs, thus the above equation can be rewritten in the following form:
This equation implies the dependencies in the process of example selection, which means that the examples selected first may influence the selection process afterwards. However, practice, due to the high computational cost, is not necessary to model the full dependency. Empirical results show that it is more efficient to model example retrieval as Markov process, where the selection of current example only depends target context and previously selected example:
PLM(Ui H, Ve)) PLM K,C, K.C.C.)
(2)
where C' denotes the retrieved in-context examples given history and knowledge. Here we decompose the conditional text generation probability into the product of two terms. PP H, K) corresponds to the joint probability distribution of in-entett exam- ples where denotes the example retrieval process. However, depending on different retrieval strate- gies, the joint probability distribution of examples can be modeled differently. For instance, we can randomly sample examples from reference dataset, the joint distribution of examples and the distri- bution of history and knowledge independent, P, (C K) (C). Previous work (Liu al., 2021) shows that random sampling leads to unstable performance, and further argues to select examples that are semantically-sinne similar to the target context, i.e. calculating (, H, K). ). In this work,
P, (C K)
II C.C.H.,,K 1). (4) i=1
After examples are retrieved, we optionally re- rank them using similarity metrics, more details can be found in next subsection. Last, & demonstrations are placed in descending order according to their retrieval score and the target context is appended at the end form the final prompt that is fed LLMs. A prompt example is illustrated in Figure 3.
3.3 Diversified Example Retrieval Strategy As mentioned earlier, the selection of examples is very important, and we need to ensure both the fidelity of the selected examples and a cer- tain amount diversity. In recommender systems,

---

there is common task of multi-objective recom- mendation, such as ensuring both the relevance and the diversity of the retrieval. We drew inspiration from this (Sá et al., 2022) and design the Diverse Example Retrieval (DER) algorithm.
Experimental Setup
We mainly experimented on OpendialKG using different retrieval strategies and different numbers of in-context examples. To ensure the universality of our method, we first experiments on Ope- nAl's GPT-3.5 (text-davinci-003) via APIS verify the effectiveness and set hyyerr-prameterr Then we test our method on several LLMss We set tem- perature to in all our experiments to avoid the impact of randommess.
First, we retrieve N relevant examples Cini (Ci,C2...,CN CN > n)) based query con- text 9, where q is the combo of dialog history H and knowledge K. Notice that the initial K examples can be retrieved by any retrieval method, our goal is find diverse subset Caivr = {C1, c, Cn. Next we find the example with the highest retrieval score and put it into a queue. We calculate the distance between the remaining examples and the example at the end of the current queue, and get the diverse score S according to (5)( The final score is linear combination of distance score and retrieval score controlled by hyper-parameter w:
4.1 Dataset & Preprocessing
OpendialKG is currently the only public open- domain English dialog dataset that is annotated with knowledge graph path according Yu al. (2022). also provides the textual form of knowl- edge corresponding to each knowledge triplet. A series of previous works have validated the effec- tiveness on this dataset (Ji et al., 2022; Dziri et al., 2021; Sarkar et al., 2022), we choose to experi- ment this dataset well. We also experimented WOW(Dinan et al., 2018) which only contains textual knowledge and yields similar results. Previ- ous works filter OpendialKG by keeping dialogue samples that are annotated with at least one knowl- edge path. We do additional filtering to ensure that at least one knowledge path is actually used in the generated responses. We hope to select demon- strations that genuinely reflect how knowledge is leveraged and thus serve better examples.
Sdiveree (C;) =(1 w) * distunce Clast) + w rT (C.,
(5)
where q denotes the query context, ret the retrieval score and Clast the last example in the queue. At last, we put the example with the highest diverse score at the end of queue. We repeat this process until there are n examples in the queue. In this way. the selected examples are both relevant to the input and distinct from the ones that have already been chosen. Algorithm 1 is pseudo-code description of DER.
Algorithm 1: Diverse Example Retrieval
4.2 Language Models
Data: Initial Retrieved Examples
We explore the effectiveness of proposed framework primarily on -ee-a.5text--davnnii 003) (Ouyang et al., 2022) which are served as black-box language models through APIs!. The currently most advanced LLMs such GPT44 (OpenAI, 2023) is included in test as well. We also experimented Vicuna-13B' and GPT-J (Wang and Komatsuzaki, 2021). The selection of black-box language models is not the primary focus of this study; rather, we seek to investigate IKA's universality.
Cini {C1, C2 , Cn JN*> n),
Empty Queue Q, Query Context q.
Example Number
Result: Diverse In-context Examples:
Cai = {Ci, C2,..., Cu)
start;
open-source
Find example O with the highest retrieval
score: Q. bacck(ckkk C/Ci; while length(() Q) do
Cromp Q1-1];
Sciot dist (Cremp. C/Cremp);
Soim* f sim(q, C/Ctemp);
4.3 Retrieval Methods for IKA
7
(1 -1-10 w) Saiv + w * Ssimi
Sparse Retrieval
8
r t arg max S:
Sparse retrieval techniques are class of traditional methods. Common sparse retrieval methods in-
Q-push_bar back (C,), (,,,CC C/Cri
end
return Q:
'hups://plaforr.oppeni.ccm http:://wwgnnnaaaac..ooo///ollle
HF

---

Model
#Parameters
Open Source
Diverse Example Retrieval
GPT-2 (2019)
1.5B
DER is implemented as described 3.3. Note that the initial example set can be retrieved by any method, for simplicity we choose dense and hybrid retrieval described above. For distance measure, we choose RoBERTa (Liu et al., 2019) as the text encoder to obtain the dense vector representation and calculate the cosine distance between examples. The embeddings produced by RoBERTa capture semantic similarities between words and sentences, which has been proved to be beneficial for informa- tion retrieval tasks.
GPT-J (2021)
6B
Vicuna(2023)
13B
GPT-3.5(2023)
175B
X
GPT-4 (2023)
unknown
Table 1: A wide variety of large language models with parameters ranging from 1.5B to 175B are chosen in our experiments.
clude TF-IDF and BM25 (Robertson and Walker, 1994). By arranging the documents into an inverted index, where each different word has an inverted list that contains information about the articles it appears in, this type of retrieval model can swiftly process queries. However, they often fall short of matching related words (Metzler and Croft, 2005) that similar in meaning. Recent works have proposed to use contextualized representation to leverage the power of deep learning language mod- cls (Gao et al., 2021).
4.4 Baselines
We compare following strong baselines to demon- strate the efficacy of our framework. RHO (Ji et al., 2022) fine-tunes BART (Lewis et al., 2019) and GPT-2 (Radford et al., 2019), introduces local and global knowledge grounding techniques with an ad hoc re-ranking procedure. They report SOTA results on OpendialKG dataset. Other strong base- lines are Sarkar et al. (2022) and Kang et al. (2022).
Dense Retrieval
4.5 Evaluation Metrics
In dense retrieval, queries and documents often directly encoded by large neural models into single multiple vectors, which allow for efficient dense retrieval based on simple similarity function such as dot- dot-product or ANN. These vectors compress high dimensional semantic information.
General Generation Evaluation
We conduct automated evaluation to measure the general generation quality and knowledge faithful- ness of responses. Traditional word-overlap based metrics such as BLEU(Papineni et al., 2002) and ROGUE-L(Lin, 2004) are adopted. Mcanwhile, some pre-trained models also exhibit promising results in term of evaluating generation informa- tiveness, fluency and relevance, one among them is BA EARTScoreeYYin Yuan et al., 2021), which formulates evaluating text as a generation task and achieves better performance than compared metrics.
Four types of dense retrieval are implemented in experiments, the first three are bi-directional contextual language models (BERT (Devlin et al., 2018), RoBERTa (Liu al., 2019), BART (Lewis al., 2019)) using [CLS] embedding as the final input representation. The same encoder is used for both query and document. We also implement TCT- ColBERT (Wang et al., 2022) using index service provided by Pyserini and FAISS libraries.
Faithful Generation Evaluation
For evaluating faithfulness, we consider Entity Coverage and FeQA et al., 2020). Ji al. (2022) argue that entities in generated responses should be covered by ones appear in dialog his- tory and knowledge. Based on the entity collection of Freebase(Bollacke) et al., 2008), we use exact match to extract named entities and calculate En- tity Precision, Recall and F1 score. In prior works (Ji et al., 2022; Dziri al., 2021), question answer- ing based metrics for faithfulness like FeQA are applied. FeQA first generates QA pairs from sum- mary and then extracts answers from the document. We concatenate dialog history and knowledge as
Dense-Sparse Hybrid
The hybrid method can capture both lexical similar- ity and high-level semantic relevance. We further show that combining dense retrieval with sparse retrieval can improve performance. Following Lin and Ma (2021), TCT-CoIBERT and Uni-COIL are chosen as dense retriever and sparse retriever in our hybrid setting. Through linear combination of scores, we adjust them to have the same order of magnitude.

---

Entity Coverage %
Model
BLEU41
ROUGE-L
BSt
FeQAt
Avg Len
Pre
Recall
F1
GPT-2 0-shot
28.51
23.44
4.01
80.67
40.40
53.84
41.65
11.88
GPT-J O-shot
39.37
29.99
4.02
87.70
36.28
51.33
44.27
13.29
Vicuna 0-shot
37.65
28.60
5.02
59.86
56.41
58.09
24.04
26.10
GPT-3.5 O-shot
46.38
34.86
5.37
76.69
69.74
73.05
43.74
16.12
NPH (2021)
10.41*
29.93*
95.61*
33.39*
53.96*
RHO (2022)
39.62
37.37
4.89
81.13
47.57
59.97
44.97
9.76
IKA+GPT-2
27.14
20.92
3.65
53.15
27.19
35.98
23.76
12.36
IKA+GPT-J
40.45
31.00
5.04
57.86
40.70
47.79
31.02
16.45
IKA+Viunna
50.28
41.14
7.03
65.27
58.63
61.77
31.43
17.63
IKA+GPT-3.5
58.11
47.09
10.90
79.12
77.41
78.26
44.67
17.08
IKA+GPT-4 49.44
39.45
7.12
73.33
77.73
75.47
37.99
21.07
Gold Res.
100
100
47.38
100
100
100
49.73
17.75
Table 2: Experimental results on OpendialKG under different settings. For experiments with IKA, we use hybrid retrieval described 4.3, the number examples set to 3. "The results of NPH are reported in (Ji et al., 2022); the results of RHO(Ji et al., 2022) calculated from their publicly available generated results., tBS stands for BARTScore; Gold Res. stands for ground truth response.
the document and response the summary sure whether the generated response is faithful to the original context(Dzzri et al., 2021).
Row 5 and Row 6 in Table 2 correspond to pre- vious SOTA. The performance of our framework when paired with various LLMs is presented in the next five lines. Our framework outperforms SOTA method (Ji et al., 2022) on general genera- tion metrics and faithful generation metrics, with a significant rise of 46.67 % in BLEU4, 26.01 % in ROUGE-L, 122.90 % in BARTScore and 30.50% in Entity Coverage Fl. In order to keep the results comparable, we filter and split our training and test set so that they are both subsets of al. (2022). We only evaluate on the common test samples Table 2.
5 Results and Analysis
The experimental results on OpendialKG are sum- marized Table 2 and Table 3. The performance of IKA is compared to previous training-base meth- ods and zero-shot baselines.
5.1 Main Results
IKA greatly improves knowledge grounded dia- logue generation quality. Table summarizes the results experiments on previous SOTA model and LLMs. The top four rows report the zero- shot performance of different LLMs, and GPT-3.5 achieves the best generation performance among them, 46.38 in BLEU4, 34.86 in ROUGE-L and 5.37 in BARTScore,, which is in line with recent studies (Hendy et al., 2023). We also report the average length generated outputs. Vicuna gener- ates the longest 26.10 10 words under O-shot setting, which results in the lowest FeQA score. It seems that Vicuna has the tendency to produce redundant nonsense. However when we combine Vicuna with IKA, the average length of generated response sig- nificantly decreases while all evaluation metrics in- observed across
The retrieval method vital to the perfor- mance of IKA. In Table 3, we report the results of several types of retrieval methods. Here we fix the retrieved example number to 3. The first row of Table 3 is random baseline. Although our refer- ence dataset has been filtered in advance which means even random sampling can retrieve rela- tively high quality examples, the improvement ob- tained is still insignificant compared to the zero- shot baseline reported in Table 2. If we choose a proper retrieval method such as BM-25 (Robertson and Walker, 1994) or TCT-ColBERT (Wang et al., 2022) the performance will boost. The hybrid trieval method achieves the highest performance in BLEU4, ROUGE-L and BARTScore, with the rise of 18.43% % 27.75% and 69.78% % respectively compared to random baseline.
crease. Similar improvements different backbone I.LMs except for GPT-2. One possible explanation may be the lack of ICL ability for smaller language models like GPT-2.
Our diverse strategy improves models' faith-

---

Entity Coverage %
Retrieval Methods
BLEU4T
ROUGE-L1
BS
FeQA*
Pre.
Recall
F1
Random
47.86
36.04
5.99
80.46
71.44
75.68
43.01
Sparse
BM-25
51.98
41.20
7.59
80.46
73.40
76.77
43.71
Uni-COIL
56.60
45.97
9.97
80.85
75.06
77.85
44.15
BERT
55.63
39.42
7.14
81.09
75.36
77.79
43.94
RoBERTa
46.42
35.74
5.66
82.03
69.27
75.11
47.65
Dense
BART 48.49
37.03
6.20
81.13
70.20
75.27
46.32
TCT-CBT
50.67
39.06
6.67
82.03
73.15
77.33
44.12
Hybrid
Hybrid
56.68
46.14
10.17
81.32
75.53
78.32
44.67
U-C div 56.17
45.61
9.99
81.14
75.58
78.26
44.80
Diverse
Hybrid div 52.89
42.34
7.85
84.47
74.21
79.01
48.08
Table 3: Comparison of different retrieval methods. Only top-3 retrieval results used in the final prompts. Random sampling serves as the baseline. Among all retrieval methods the hybrid method achieves the highest scores on general quality metrics, whereas DER further improves faithfulness metrics while sacrificing generation metrics.
Triplet
Text
sonable, since these metrics only measure word- overlapping in discrete string space. In summation, metrics explicitly measure how well models frameworks can generate faithfully are needed.
#EX
BS
EC-F1
BS
EC-F1
6.48
74.97
5.99
74.23
10.90
77.16
9.99
75.91
13.16
77.62
12.52
77.50
80
10
15.12
77.25
14.38
77.82
Gneration Metrics
Table 4: Comparison of knowledge representation.
60
fulness generation ability. In Section 3.3 we pro- pose the diverse strategy for ICL example selection. Since involves the trade-off between relevance and diversity controlled by the hyper-parameter w,
BLEU-4
ROUGE-L
10
EC-FI
additional syper-parameter search is required in practice. Due resource constraint we empirically set value for w based on performance on dev set, and it is able to achieve the highest Entity Cover- age F1 at 79.01 and FeQA at 48.08 with a slight drop on general quality metrics. A HP search for coefficients may further boost the performance of DER.
0
01 3
5
10
16
Number of Examples
Figure 4: Generation metrics for IKA under different number of examples.
On the Impact the Number of Examples
5.2 Analysis
According to Brown al. (2020), the number of demonstrations is an important factor in ICL. To test the scalability of IKA and also keep the con- structed prompt within context window size, we experiment the number of examples from 0 to 16. Figure 5.2 illustrates the results. As the number grows, IKA generates more high quality response; but the EC-FI metric rises and then falls, which means there might be trade-off between faithful- ness and the generation quality. The more examples provided, the better L.LMs can mimic conversation style which contributes to the improvements in eval- uation metrics.
this section, we give some analysis on different aspects of our IKA framework.
On the High Scores of Generation Metrics
The BLEU and ROUGE scores in Table 2 and 3 may seem extraordinary high for a text generation task. The task of knowledge grounded dialog gen- eration focuses on the usage of knowledge. Its task structure inherently leads relatively fixed outputs, as the majority of the information de- rived from the input knowledge. Having high BLEU/ROUGE score for such a task is quite rea-

---

Ret.
#EX
BLEU4
ROUGE-L
BS
assume that the gold relevant knowledge is pro- vided in target context. How LLMs integrate knowl- edge also under-studied. Due text window size constraints, we are unable to obtain results for more examples. Last, we do not perform human evaluation to analyze different types of faithfulness errors in our generated results. We leave these open questions for future work.
None
0
13.99
9.39
2.92
Rand.
3
58.09
58.62
23.15
BM25
3
60.35
61.09
24.78
Table Results of -tte---rrnnttt on Wizard-of- (Diieeaa et al., 2018) unseen split results.
On the Choice of Knowledge Representation We compare the performance of IKA given dif- ferent forms of knowledge including knowledge graphs and texts. Results on OpendialKG are pre- sented in Table 4. Surprisingly the knowledge triplet is almost always better no matter how many examples are presented. One possible reason is that the knowledge triplct is more suitable for example retrieval given their succinet triplet form, while textual knowledge may provide redundant infor- mation. Besides, the structured form is more com- pact which passes the most essential knowledge LLMs. Results on Wizard-of- -iiiiiieediii (Dinan et al., 2018) are summarized in Table 5, where only textual knowledge is provided. Under 3-example setup, we compare random retrieval and BM25 re- trieval. On unseen test split of WoW, BLEU and ROUGE-L increase from 58.09 to 60.35 and 58.62 to 61.09 respectively, BARTScore increases from 23.15 to 24.78. These results demonstrate the uni- versality of our framework across different forms and areas of knowledge.
Ethics Statement
Our work complies with the ACL Ethics Policy. All datasets and models are publicly accessible except for OpenAl's text-davinci-003 and GPT-4. We have not identified any significant ethical considerations associated with our work. We believe our findings can help reducing LLMs' hallucinations.
Acknowledgement
We thank the anonymous reviewers for their helpful comments on this paper. This work was partially supported by National Key R&D Program of China (No. 2022YFC3600402) and National Social Sci- ence Foundation Project of China (21&ZD287). The corresponding author of this paper is Sujian Li.
References
Ekin Akyürek, Dale Schuurmans, Jacob Andreas. Tengyu Ma, and Denny Zhou. 2022. What learning algorithm is in-context learning? investigations with linear models. arXiv preprint 1rXiv:2211.1506
6 Conclusion
Ebtesam Almazrouci, Hamza Alobeidli, Abdulaziz Al- shamsi, Alessandro Cappelli, Ruxandra Cojocaru, Mer- ouane Debhah, Etienne Goffinet, Daniel Heslow, Julien Launay, Quentin Malartic, Badreddine Noune, Baptiste Pannier, and Guilherme Penedo. 2023. Falcon-40B, an open large language model with state-of-the-arr perfor- mance.
In this paper, we explore the strategy of adopting in-context learning for knowledge grounded dialog generation. We propose a plug-and-pll retrieval based framework IKA that can be easily integrated with LLMs. Our framework significantly outper- forms strong baselines and previous SOTAs with- out additional parameters update. We explicitly model the dependency between ICL examples and design a diversification strategy that further im- proves performance. Experiments demonstrate the effectiveness and generalization our framework. Our results also provide the evidence for ICL's po- tentials in knowledge-intensive tasks.
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008. Freebase: collabo- ratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 1247-1250.
Tom Brown, Benjamin Mann. Nick Ryder, Melanic Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901.
Limitations
In this work, we do not study the knowledge selec- tion process which is also important to knowledge grounded generation. Following previous works,
Jacob Devlin. Ming-Wo Wei Chang. Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep

---

bidirectional transformers for language understanding, arXiv preprint arXiv:1810.04805.
Ziwei Ji. Zihan Liu, Nayeon Lee, Tiezheng Yu, Bryan Wilie, Min Zeng, and Pascale Fung. 2022. Rho Reduc- ing hallucination in open-domain dialogues with knowl- edge grounding. arXiv preprint arXiv:2212.01588
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan. Michael Auli. and Jason Weston. 2018. Wizard of wikipedia: wwwwwldge-powerree conversational agents. arXiv preprint ar 181.0024)
Minki Kang, Jin Myung Kwak, Jinheon Baek, and Sung Ju Hwang. 2022. Knowledge-consistent dialogue generation with knowledge graphs.
Esin Durmus, He He, and Mona Diab. 2020. Feqa: question answering evaluation framework for faithful- ness assessment in abstractive summarization. arXiv preprint 03574
Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fan- njiang, and David Sussillo. 2018. Hallucinations in neural machine translation.
Nouha Dziri, Ehsan Kamalloo, Sivan Milton, Osmar Za- iane, Mo Yu, Edoardo M Ponti, and Siva Reddy. 2022a. Faithdial: faithful benchmark for information-seekinn dialogue. Transactions of the Association for Computa- tional Linguistics, 10: 14:1499-4
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad. Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: De- noising sequence-to-sequeen pre-training for natural language generation, translation, and comprchension. arXiv preprint arXiv: 13461.
Nouha Dziri, Andrea Madotto, Osmar Zaiane, and Avishek Bose. 2021. Neural path hunter: Reducing ballucination in dialogue systems via path grounding. arXiv preprint arXiv:2104.08455.
Chin-Yew Lin. 2004. Rouge: A package for auto- matic evaluation of summaries. In Text sunmarization branches out, pages 74-81.
Nouha Dziri, Sivan Milton, Mo Yu, Osmar Zaiane, and Siva Reddy. 2022b. On the origin of hallucinations in conversational models: Is the datasets or the models? arXiv preprint arXiv:2204.099.
Jimmy Lin and Xueguang Ma. 2021. A few brief notes on deepimpact, coil, and a conceptual framework for information retrieval techniques. arXiv preprint arXiv: 2 106. 14807.
Linnea Evanson, Yair Lakretz, and 2023. Language acquisition: do children and language models follow similar learning stages? arXiv preprint 230.0860...
King.
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021. What makes good in-context examples for gpt-3? arXiv preprint
Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021. Coil: Revisit exact lexical match in information re- trieval with contextualized inverted list. arXiv preprint arXiv:2/04.07186
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint 1907.
He He, Anusha Balakrishnan, Mihail Eric, and Percy Liang. 2017. Learning symmetric collaborative dia- logue agents with dynamic knowledge graph embed- dings. arXiv preprint arXiv: Mixi::170..0 700.007130.
Donald Metzler and W Bruce Croft. 2005. A markov random field model for term dependencies. Pro- ceedings of the 28th annual international ACM SIGIR conference Research and development in information retrieval, pages 472-479.
Amr Hendy, Mohamed AbdeIrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokazu Matsushita, Young Jin Kim, Mohamed Afify, and Hany Hassan Awadalla. 2023. How good are gpt models at machine translation? comprehensive evaluation. arXiv preprint arXiv:2302.099110
Sabrina Mielke, Arthur Szlam, Emily Dinan, and Y- Lan Boureau, 2022. Reducing conversational agents' overconfidence through linguistic calibration. Transac- tions the Association for Computational Linguistics, 10:857-872.
Zhiting Hu and Li Erran Li. 2021. A causal lens for controllable text generation. Advances in Neural Infor- mation Procesing Systems, 34:24941-24955
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettle- moyer. 2022. Rethinking the role of demonstrations: What makes in-context learning work? arXiv preprint MxXiv:2202.12831
Yichong Huang, Xiachong Feng, Xiaocheng Feng, and Bing Qin. 2021. The factual inconsistency problem in abstractive text summarization: A survey. arXiv preprint 2rXiv:2104. 14839.
Seungwhan Moon, Pararth Shah, Anuj Kumar, and Ra- jen Subba. 2019. Opendialkg: Explainable conversa- tional reasoning with attention-based walks over knowl- edge graphs. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 845-854.
Ziwei Ji. Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation, ACM Computing Surveys, 15(1)).

---

Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. 2022. In-context learning and induction heads, arXiv preprint 1.X
Ben Wang and Aran Komatsuzaki. 2021. J-6B: A 6 Billion Parameter Autoregressive Lan- guage Model. https://gi www.tttw.....nggeeeeeee com/kingoflolz/ mesh- tesh-traasformer-jaa
GPT-
Xiao Wang. Sean MacAvaney, Craig Macdonald, and ladh Ounis. 2022. An inspection of the reproducibility and replicability of tct-colbert. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2790- 2800.
OpenAI. 2023. Gpt-4 technical report.
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022. Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems, 35:27730-27744.
Xinyi Wang, Wanrong Zhu, and William Yang Wang. 2023. Large language models are implicitly topic mod- els: Explaining and finding good demonstrations for in-context learning. arXiv preprint arXiv:2301.119
Kishore Papineni, Salim Roukos, Todd Ward, and Wei- Jing Zhu. 2002. Bleu: a method for automatic evalua- tion of machine translation. In Proceedings of the 40th annual meeting of the Association for Computational Linguistics, pages 311-318.
Zeqiu Wu, Michel Galley, Chris Brockett, Yizhe Zhang. Xiang Gao, Chris Quirk, Rik Koncel-Kedzione Jian- feng Gao, Hannanch Hajishirzi. Mari Ostendorf, et al. 2021. A controllable model of grounded response gen- eration. Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 14085-14093.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAl blog, 18)::
Yan Xu, Etsuko Ishii, Samuel Cahyawijaya, Zihan Liu, Genta Indra Winata, Andrea Madotto, Dan Su, and Pas- cale Fung. 2021. Retrieval-free Knowledge-grounet grounded dialogue response generation with adapters. arXiv preprint arXiv:2105.06232.
Stephen E Robertson and Steve Walker. 1994. Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. In SIGIR '94: Pro- ceedings the Seventeenth Annual International ACM- SIGIR Conference on Research and Development in Information Retrieval, organised by Dublin City Univer- sity, pages 232-244 232-241. Springer.
Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang. Heng Ji, and Meng Jiang. 2022. A survey of knowledge-enhance Computing Surveys, 54(1 1s):1-38.
text generation. ACM
João Sá. Vanessa Queiroz Marinho, Ana Rita Magal- hães, Tiago Lacerda, and Diogo Goncalves. 2022. Di- versity vs relevance: A practical multi-objective study in luxury fashion recommendations, In Proceedings the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages
Weizhe Yuan, Graham Neubig, and Pengfei Liu. 2021. Bartscore: Evaluating generated text as text generation. Advances in Neural Information Processing Systems, 34:27263-2722 27277.
Houyu Zhang, Zhenghao Liu, Chenyan Xiong, and Zhiyuan Liu. 2019. Grounded conversation generation as guided traverses in commonsense knowledge graphs arXiv preprint
Rajdeep Sarkar, Mihacl Arcan, and John Philip McCrae. 2022. Kg-cruse: Recurrett walks over knowledge graph for explainable conversation reasoning using semantic embeddings. In Proceedings of the 4th Workshop on NLP for Conversational AI, pages 98-107.
Hao Zhou, Minlie Huang. Yong Liu, Wei Chen, and Xiaoyan Zhu. 2021. Earl: Informative knowledge- grounded conversation generation with entity- antity-agnostic representation learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 2383-2395.
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021. Retrieval augmentation re- duces hallucination in conversation. arXiv preprint arXiv:2104.07567
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Ali- cia Jin, Taylor Bos, Leslie Baker, Yu Du, et al. 2022. Lamda: Language models for dialog applications. arXiv preprint arXiv:2201.0823:
Nitasha Tiku. 2022. The google engineer who thinks the company's ai has come to life. The Washington Post, 11.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30.


Reviews:
[{"review_id": "ngtqHgp2bB", "reviewer": "Reviewer_MTiQ", "summary": "", "questions": "1. Why simple linear combination of dense retrieval score and sparse retrieval score works? It seems their scores' distribution and range are different.\n2. The too-high BLEU/ROUGE score is unnormal -- BLEU-4 ≈ 50 is crazy. It usually means the dataset may not be capable of reflecting the practical performance of methods, or the task has been well solved.", "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 appreciate your feedback and insights on our paper. \n\nWe acknowledge the importance of providing deeper insights and analyses to substantiate the effectiveness of our method.  Due to page limit, we were unable to present our full analysis of reasons behind the observed improvements and the influence of different retrieval methods. We will enhance the analysis section in revised version of our paper.\n\nThe reasons we chose OpendialKG over Wizard-of-Wikipedia are as follows:\n\n1. The main difference between OpendialKG and Wizard-of-Wikipedia is that OpendialKG provides two forms of knowledge representation, both triplet and texual. We experimented on both forms, the results are presented in Table4.\n2. The quality of dataset. The Wizard-of-Wikipedia training set contains 18340 dialogs, but some of the samples in test set also appear in training set so there is data leak. In addition, about 50% of wizards' responses do not utilize any knowledge, even if they do, many of the referenced knowledge are imcomplete segments from wikipedia. According to [1], 60%~70% of WoW responses are hallucinated or partially hallucinated. Thus we doubt the choice of WoW as a fair evaluation.\n\nDespite the problems mentioned above, we conducted additional experiments on WoW. Below are our results on WoW unseen test set. The first column stands for different retrieval methods, the scond column stands for different number of in-context-learning examples, the last two columns are BLEU and ROUGE-L scores.\n\n| Retrieval | #EX  |    BLEU    |  ROUGE-L   | BARTScore  |\n| :-------: | :--: | :--------: | :--------: | :--------: |\n|   None    |  0   |   0.1399   |   0.0939   |   2.9168   |\n|  Random   |  3   |   0.5809   |   0.5862   |   23.146   |\n|   BM25    |  3   | **0.6035** | **0.6109** | **24.782** |\n\nWe filter out wizards' responses that do not provide knowledge in both training set and test set.  All ICL examples are retrieved from the training set. For comparison, we also choose text-davinci-003 as the base LLM. Under 0-shot setting, the BLEU/ROUGE-L/BARTScore are 0.1399/0.0939/2.9168, which demonstrates the great potential of in-context-learning in KGD. Under 3-example setting, we compare random retrieval and BM25 top-k retrieval. On unseen test split of WoW, BLEU and ROUGE-L increase from 0.5809 to 0.6035 and 0.5862 to 0.6109 respectively. BARTScore increases from 23.146 to 24.782. We will append case study in the revised version.\n\nAnswer to Q1: Why simple linear combination works?\n\nAbout the hybrid retrieval, we basicly follow the setting reported in [2]. The scores range for dense retrieval and sparse retrieval is different, but they are on the same order of magnitude. The retrieval score is calculated as alpha \\* sparse_score + beta \\* dense_score, where alpha and beta are 0.5 and 1 respectively. We choose hyper-parameters according to performance on validation set. More experimental details will be added in appendix, including the HP search process and model choice.\n\nAnswer to Q2: Too-high score means the dataset is uncapable?\n\nThe task of knowledge grounded dialog generation focuses on the usage of knowledge. This task structure inherently leads to relatively fixed outputs, as the majority of the information is derived from the input knowledge. Unlike open domain dialog generation that aims for diversity, task of this nature prioritize accuracy in conveying the correct knowledge. Having a high BLEU/ROUGE score for such a task is quite reasonable, since these metrics only measure word-overlapping in discrete string space.\n\nWe provide other evaluation metrics such as BARTScore and FeQA which leverage more pre-trained parameters and more robust compared to BLEU/ROUGE. Besides, the faithfulness of dialog generation is another important aspect we consider, which can not be reflected by BLEU or ROUGE scores. In terms of these metrics, KGD task is far from solved. \n\nAgain, thank you for your constructive feedback, we are dedicated to addressing these issues!\n\n[1] Dziri, Nouha, et al. \"On the origin of hallucinations in conversational models: Is it the datasets or the models?.\" *arXiv preprint arXiv:2204.07931* (2022).\n\n[2]Lin, Jimmy, and Xueguang Ma. \"A few brief notes on deepimpact, coil, and a conceptual framework for information retrieval techniques.\" *arXiv preprint arXiv:2106.14807* (2021).", "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 method is intuitive, including retrieving both diverse and relevant demonstrations and using both dense and sparse retrieval methods.\n2. The experiment indicates the method outperforms other methods in the OpendialKG dataset.", "reasons_to_reject": "1. Although the method improves several metrics, few insights and analyses are presented. The research needs more analysis to prove the effectiveness, such as why the method improves in-context learning, why different retrieval method has different performances, which demonstrations are preferred by LLMs, and showing cases to help compare and understand why the retrieved demonstrations improve performance.\n2. The retrieval framework is not designed for knowledge graph retrieval but is only evaluated on OpendialKG. Thus, knowledge-grounded dialogue generation datasets like Wizard-of-Wiki, grounding on documents, are worth evaluating.", "questions_to_authors": "1. Why simple linear combination of dense retrieval score and sparse retrieval score works? It seems their scores' distribution and range are different.\n2. The too-high BLEU/ROUGE score is unnormal -- BLEU-4 ≈ 50 is crazy. It usually means the dataset may not be capable of reflecting the practical performance of methods, or the task has been well solved.", "paper_topic_and_main_contributions": "This paper proposes a plug-and-play retrieval method to improve in-context learning of knowledge-grounded dialogue generation. It retrieves both diverse and relevant demonstrations in a Markov process manner.", "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": {}}, {"review_id": "HTlzdDVRxv", "reviewer": "Reviewer_WFsR", "summary": "", "questions": "1. Is Table 3 comparable to Table 2?  Aside of different retrieval methods, what is the LLM used for Table 3?\n2. When retrieving examples for in-context learning, what is the source?  Does it only retrieve from the training set samples or the whole set?", "limitations": "", "rating": 4, "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 greatly appreciate your recognition of our work and valuable feedback!\n\nIn response to your concern, we admit our reliance on powerful LLMs, while models like GPT-2 or GPT-J are usually used in a finetuned manner. The difference may also come from the training corpus and parameter size of LLMs, which is hard to control in our setting. Our method does have some scope for use, we will clarify this point in our revised version. Thank you for helping us refine our work and clarify the role of GPT-3.5 in our proposed method's success.\n\nAnswer to Q1: Is Table 3 and Table 2 comparable? What LLM?\n\nYes, the results in Table 3 is comparable to Table 2. We used text-davinci-003 for Table 3.\n\nAnswer to Q2:  What is the source for retrieval?\n\nWe retrieve examples from training split rather than the whole set. Training set consists of held-out dialogs that have no overlap with the test set.\n\nWe appreciate the insights provided and are committed to addressing these issues in our revised 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": "1. The integration of in-context learning into KGD, and the transformation of the knowledge encoding and dialogue generation problem into an example retrieval challenge, represent a novel and intriguing approach. The potential implications of this work for future research in this domain are significant and valuable.\n\n2. The IKA framework proposed in this study has demonstrated a remarkable improvement in the state-of-the-art performance, achieving an impressive 46.67% increase in BLEU4 and 26.01% in ROUGE-L scores. While part of these gains can be attributed to the utilization of GPT 3.5, Table 2 highlights IKA's substantial enhancement of GPT 3.5 in the KGD task. Extensive ablation experiments underscore the importance of retrieval quality for performance, and the proposed diverse example retrieval strategy stands out with its highest entity coverage and FeQA scores. These findings provide compelling evidence of the efficacy of the IKA framework in pushing the boundaries of performance in knowledge-grounded dialogue generation.", "reasons_to_reject": "The remarkable improvements achieved can largely be attributed to the use of GPT 3.5. In Table 2, IKA does not yield significant additional gains when applied to GPT2 and GPT-J. Its benefits become evident only when combined with Vicuna and GPT 3.5. Furthermore, the comparison in Row 4 of Table 2 highlights the substantial superiority of GPT 3.5 0-shot over the previous SOTA. An educated assumption suggests that GPT 3.5 likely possesses a wealth of external knowledge beyond the training data, making it an unequal comparison with the previous SOTA and the proposed methods. These observations collectively underscore the heavy reliance of the proposed method on a powerful LLM like GPT 3.5.", "questions_to_authors": "1. Is Table 3 comparable to Table 2?  Aside of different retrieval methods, what is the LLM used for Table 3?\n2. When retrieving examples for in-context learning, what is the source?  Does it only retrieve from the training set samples or the whole set?", "paper_topic_and_main_contributions": "The primary focus of this paper is on constructing a knowledge-grounded dialogue (KGD) system. While existing LLM-based KGD systems lack full control over generated content and may sometimes experience hallucinations, this paper presents an innovative approach. The author tackles the knowledge-based dialogue generation problem by transforming it into an example retrieval challenge using off-the-shelf large language models (LLMs) and in-context learning.\n\nTwo significant contributions stand out: firstly, this work pioneers the modeling of KGD within the in-context learning framework. Secondly, the authors propose a retrieval-based framework that outperforms the previous state-of-the-art (SOTA) by a substantial margin. By leveraging these novel advancements, the paper showcases a promising path for more effective and controlled knowledge-grounded dialogue generation.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "n/a", "Typos_Grammar_Style_and_Presentation_Improvements": "n/a"}, "extra_text": {}}, {"review_id": "d8GBxlV8u5", "reviewer": "Reviewer_fq1q", "summary": "", "questions": "1. How do you calculate the distance between each example in DER?\n2. While you approach leverage existing datasets with knowledge graph, how about the performance on the datasets without knowledge graph?\n", "limitations": "", "rating": 3, "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": "Thank you for the constructive review! We will fix the typo on line 301 and add clarification for the AVG column in Table 2 immediately.\n\nRegarding the novelty of our method, we do not regard the combination of LLM and retrieval is trivial. This is because while the training process of LLMs does not encompass retrieval, the inference of LLMs heavily leans on the retrieval module, especially in the context of knowledge grounded dialog generation. We have clarified this distinction in the paper, highlighting how our combined approach offers a new perspective and SOTA results in this context.\n\nAnswer to Q1: How to calculate distance in DER?\n\nWe use RoBERTa as a text encoder to obtain the vector representation of in-context-learning samples, then we use consine distance to calculate the distance between each example. The embeddings produced by RoBERTa capture semantic similarities between words and sentences, which has been proved to be beneficial for information retrieval tasks.\n\nAnswer to Q2: How about performance on dataset without knowledge graph?\n\nWe did conduct experiments that did not use knowledge graphs. We use the textual form of knowledge, originally provided in Opendialkg dataset. The results of these experiments are reported in Table4. We plan to emphasize in revised version that our experiments are not only performed on the knowledge graphs.\n\nThe reasons we chose OpendialKG over Wizard-of-Wikipedia are as follows:\n\n1. The main difference between OpendialKG and Wizard-of-Wikipedia is that OpendialKG provides two forms of knowledge representation, both triplet and texual. We experimented on both forms, the results are presented in Table4.\n2. The quality of dataset. The Wizard-of-Wikipedia training set contains 18340 dialogs, but some of the samples in test set also appear in training set so there is data leak. In addition, about 50% of wizards' responses do not utilize any knowledge, even if they do, many of the referenced knowledge are imcomplete segments from wikipedia. According to [1], 60%~70% of WoW responses are hallucinated or partially hallucinated. Thus we doubt the choice of WoW as a fair evaluation.\n\nDespite the problems mentioned above, we still conducted additional experiments on WoW. Below are our results on WoW unseen test set. The first column stands for different retrieval methods, the scond column stands for different number of in-context-learning examples, the last three columns are BLEU, ROUGE-L and BARTscores.\n\n| Retrieval | #EX  |    BLEU    |  ROUGE-L   | BARTScore  |\n| :-------: | :--: | :--------: | :--------: | :--------: |\n|   None    |  0   |   0.1399   |   0.0939   |   2.9168   |\n|  Random   |  3   |   0.5809   |   0.5862   |   23.146   |\n|   BM25    |  3   | **0.6035** | **0.6109** | **24.782** |\n\nWe filter out wizards' responses that do not provide knowledge in both training set and test set. For comparison, we also choose text-davinci-003 as the base LLM. Under 0-shot setting, the BLEU/ROUGE-L/BARTScore are 0.1399/0.0939/2.9168, which demonstrates the great potential of in-context-learning in KGD. Under 3-example setting, we compare random retrieval and BM25 top-k retrieval. On unseen test split of WoW, BLEU and ROUGE-L increase from 0.5809 to 0.6035 and 0.5862 to 0.6109 respectively. BARTScore increases from 23.146 to 24.782. Besides, we will append case study in the revised version.\n\n[1] Dziri, Nouha, et al. \"On the origin of hallucinations in conversational models: Is it the datasets or the models?.\" *arXiv preprint arXiv:2204.07931* (2022).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "They apply in-context learning and different retrieval strategies for knowledge grounded dialog generation. And their experiments demonstrate the good performance on OpendialKG dataset.", "reasons_to_reject": "Firstly, the main contribution of this paper is to leverage in-context learning (ICL) and retrieval for knowledge grounded dialog generation, but they don't have a innovated approach for both concepts. Secondly, their proposed diversified example retrieval strategy doesn't outperform over hybrid (sparse and dense) retrieval strategy overall. Lastly, they only experiment on one dataset OpendialKG which doesn't justify their approach can be generalized to other datasets or tasks.", "questions_to_authors": "1. How do you calculate the distance between each example in DER?\n2. While you approach leverage existing datasets with knowledge graph, how about the performance on the datasets without knowledge graph?\n", "paper_topic_and_main_contributions": "This paper proposes to leverage in-context learning and retrieval approach to enhance large language models on knowledge grounded dialog generation. Specifically, their prompt includes instruction, in-context examples, dialog history and knowledge. To retrieve the in-context examples, they designed a diverse example retrieval (DER) approach. They worked a knowledge grounded dialog dataset OpendialKG that has annotated with knowledge graph and experimented with different retrieval strategies and LLMs. Their results demonstrated that their plug-and-play retrieval-based framework IKA can improve knowledge grounded dialogue generation quality and the retrieval approach is vital to the performance. Their diverse retrieval strategy doesn't perform better than the sparse and dense hybrid approach, but can improve model's faithfulness generation ability.", "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": "N/A"}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "In table 2, the column AVG in the rightmost might be misleading as people might think it denotes the average performance. In line 301, you have the same notation K for relevant examples and knowledge graph."}}]

Author rebuttal:

Meta-review: Plug-and-play retrieval + in-context learning-based dialog generation for knowledge grounding. While the method is intuitive, the paper lacks a proper demonstration of how each component of the model contributes to a high performance.
Decision: Accept-Findings