Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
Authors: Hongru WANG
Rui Wang
Fei Mi
Yang Deng
Zezhong WANG
Bin Liang
Ruifeng Xu
Kam-Fai Wong
Venue: EMNLP 2023 Findings (2023)
Keywords: chain-of-thoughts
in-context learning
personalized dialogue system
empathetic dialogue system
large language models
TL;DR: A novel chain-of-thoughts to reason linguistic cues (user status) exhibited in the dialogue context, aiming to generate better responses

Abstract: Large Language Models (LLMs), such as ChatGPT, greatly empower dialogue systems with strong language understanding and generation capabilities. However, most of the previous works prompt the LLMs to directly generate a response based on the dialogue context, overlooking the underlying linguistic cues about the user status exhibited in the context. Such in-depth dialogue scenarios are challenging for existing LLMs to figure out the user's hidden needs and respond satisfactorily through a single-step inference. To this end, we propose a novel linguistic cue-based chain-of-thoughts (Cue-CoT), which enhances the LLMs inference with an intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging response. To evaluate the approach, we build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English, targeting 3 major linguistic cues during the conversation: personality, emotion, and psychology. We conducted experiments on the proposed benchmark with 5 LLMs under both zero-shot and one-shot settings. Empirical results demonstrate our proposed Cue-CoT method outperforms standard prompting methods in terms of both helpfulness and acceptability on all datasets.

Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue Questions with LLMs
Hongru Wang', Rui Wang Fei Mi;, Yang Deng", Zezhong Wang',
Bin Liang', Ruifeng Xu Kam-Fai Wong
Moo Key Laboratory of High Confidence Software Technologies,
The Chinese University of Hong Kong
Harbin Institute of Technology. Shenzhen, China, HHuawei Noah's Ark Lab *National University of Singapore, 'Peng Cheng Laboratory, Shenzhen, China
Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies
(hrwang, kfwong) cuhk. edu. hk ruiwangnlp@outlood com
Abstract
query or dialogue content to the LLM for gener- ating response with preceding prompt, mak- ing the responses stereotypical and tedious, espe- cially for in-depth dialogue questions (Zhao et al., 2023). On the contrary, is widely acknowledged that dialogue contexts generally convey a lot of information about the user status in addition to the pure semantic information from a linguistic perspective (Mairesse et al., 2007; Tausczik and Pennebaker, 2010; Schwartz et al., 2013). Specifi- cally, the linguistic cues underlying dialogue text have been shown to be effective means of revealing the emotions (Ekman, 1971), personality traits (Mairesse et al., 2007), psychological char- acteristics (Tausczik and Pennebaker, 2010), and other relevant information of users (Turney, 2002; Newman et al., 2003). Consequently, recognizing and understanding these cues exhibited in the con- text of dialogues becomes crucial to comprehend user intentions and status (Rashkin et al., 2019). By doing so, a dialogue system can generate responses that align with the user's expectations, meet their unique needs and preferences, and ultimately strive towards constructing a human-like conversational experience (Salemi et al., 2023).
Large Language Models (LLMs), such as ChatGPT, greatly empower dialogue systems with strong language understanding and gener- ation capabilitiis. However, most of the pre- vious works prompt the I.LMs to directly gen- erate a response based on the dialogue con- text, overlooking the underlying linguistic cues about the user status exhibited in the context. Such in-depth dialogue scenarios are challeng- ing for existing LLMs to figure out the user's hidden needs and respond satisfactorily through single-step inference. To this end, propose a novel linguistic cue-based chain-of- -foouughtt (Cue-CoT), which enhances ence with intermediate reasoning step to find cues exhibited in the dialogue, aiming to provide a more personalized and engaging re- sponse. To evaluate the approach, we build benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English, targeting 3 major linguistic cues dur- ing the conversation; personality, emotion, and psychology. We conduct extensive experiments on the proposed benchmark with 5 LLMs under both zero-shot and one-shor shet settings. Empiri- cal results demonstrate proposed Cue-Co'T method outperforms standard prompting meth- ods in terms of both helpfulness and acceptabil- ity on all datasets.
L.MSs infer-
Inspired by the emergent capabilities of LLMs on reasoning over texts (Wei et al., 2022), while pre- vious efforts mostly investigate in-context learning (Huang et al., 2022) or ohain-of-though prompt- ing schemes (Wei et al., 2023) on text classifica- tion (Zhang et al., 2022) and question answering (Press et al., 2022; Shum et al., 2023; Margatina et al., 2023), we take advantage of LLMs to reason for dialogue (Lee et al., 2023), which, to our best knowledge, has not been thoroughly investigated yet. Specifically, we design linguistic cue-based of -hhohughss (Cue-CoT), consisting of two variants: O-Cue CoT and M-Cue CoT in which the former one outputs intermediate reasoning re-
1 Introduction
Large Language Models (LLMs), or foundation models (Zhou et al., 2023), especially after the ap- pearance of ChatGPT recently revolutionize the paradigm of various natural language processing (NLP) tasks, including dialogue response gener- ation tasks (Bang et al., 2023). However, most existing LLM--baee studies directly feed the user
"Equal Contribution. Conresponding Author.

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Stundard Prompting
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Figure I: An example different prompting for responding to in-depth dialog questions with LLMs, including standard prompting. O-Cue CoT, and M- M-Cue CoT. We shadow the intermediate reasoning results, i.e., the personality. empathy, and psychological status of the user, and highlight the instructions at the input and indicate the roles of different parts of the response (in green) in M-Cue CoT.
sults with a final response in one-step but the latter reasons step by step, as shown in Figure 1. In detail, with standard prompting, LLLM-based sys- tems directly generate the response given the dia- logue context. Regarding the user status implied by the context intermediate reasoning results (Cue CoT), we prompt the system to infer the user status first and then generate response based on dialogue context and user status.
vanced reasoning and planning based on user statuses. Additionally, we suggest utilizing intermediate reasoning results as a criterion for selecting demonstrations in limited train- ing data scenarios, specifically in onc-shot settings.
Our findings demonstrate that both the O- Cue CoT and M-Cue CoT approaches out- perform standard prompting in generating more helpful and acceptable responses for the users. Specifically, the M-Cue CoT shows su- perior robustness and reasoning performance in all datasets and all LLMs. Furthermore, our novel demonstration selection strategy ex- hibits superior performance under both ran- dom selection and top-1 selection.
To evaluate our approach, we build a bench- mark, consisting of 6 in-depth dialogue datasets in both Chinese and English, considering three ma- jor aspects of user statuses: personality, emotions, and psychology, exhibited during the conversation, forming a comprehensive evaluation benchmark incorporating various user statuses in the context of dialogue response generation. We conduct ex- tensive experiments with 5 L.L.- dialogue systems based on the benchmark using the afore- mentioned three types of prompting schemes. To sum up. our contributions can be summarized be- low:
2 Related Work
Following the initial chain-of-thought prompting (Wei et al., 2023), lots of works spring up aim to improve different parts of original reasoning processing, including auto-cot (Zhang et al., 2022), self-consistency(YW. (Wang et al.. 2023e), active prompt (Diao et al., 2023), automate- cot (Shum et al., 2023). Besides that, a further line of work studies in-context learning (Brown et al., 2020) as its efficiency and effectiveness with LLMs as backbones in which the key of it is to select in- formative demonstrations to prepend the input as additional information to get better results (Liu et al., 2022). To find the best demonstrations and unleash LLMs' power, Liu et al. (2022) propose to retrieve examples that are semantically similar
Chain-of-thought Prompt.
We construct in-depth dialogue evaluation benchmark considering the personality, emo- tion, and psychology of users exhibited in the conversation, with the goal of aligning with unique user needs and status, which consists of 6 datasets, and 7.3k dialogues?.
We propose two effective dialogue cots: O- Cue CoT and M-Cue CoT, that enable ad-
"Our datasct and demo are released here: https:// gi github. Conm/ruleGreenCCee-re

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test query sample while some works utilize uncertainty (Diao al., 2023) or diversity (Li and Qiu, 2023) to refine and evaluate the selected exam- ples. Also, few works (Deng et al., 2023a) focus on the intermediate reasoning steps, and they use the reasoning complexity (Fu et al., 2023), i.e, chains with more reasoning steps, making the effective demonstration.
where M is parameterized by LLMs, e and r de- motes dialogue context and response respectively.
O-Cue COT. In line with the traditional chain-of- thoughts, we prompt the models to generate the middle reasoning processing and final results to- gether, for example, we can prompt the LLMs to generate user status and a final response simultane- ously giving dialogue context, enforcing the LLMs to reason based on the user status. However, it is important to note that generating intermediate rea- soning results with responses together may lead to reduction in the length of the different outputs, particularly when multiple or complex reasoning results are involved, sacrificing the details and ex- planations. For example, as shown in O-Cue CoT in Figure 1, the generated user status is too short to provide cues for responses. Moreover, it is in- feasible to modify the intermediate results when it is wrong (Wang et al., 2023c). Here, we define the objective as follows in which 8 stands for user status:
Dialogue System. Most of the previous work develops personalized (Zhang al., 2018; Zheng et al., 2020; Song et al., 2021; Chen et al., 2023a), emotional (Ghosal et al., 2020; Liu et al., 2021; Zheng et al.. 2023a; Deng et al., 2023c; Zheng et al., 2023b), empathetic (Rashkin et al., 2019; Zheng et al., 2021: Sabour et al., 2022) dialogue system in isolation, rather than seamlessly blend- ing them all into one cohesive conversational flow (Smith al., 2020; Wang al., 2023a). A com- mon approach is to predict the emotion or persona from a pre-defined set and generate the response in a multi-task manner (Ma et al., 2021; Zheng et al., 2021; Sabour et al., 2022; Cheng et al., 2023; Deng et al., 2023b). Besides that, lots of work notices these linguistic cues underneath text by directly predicting them independently as a classification task (Wang al., 2022; Barriere al., 2022: Ghosh et al., 2022). Distinguishing from these previous works, we regard different aspects cues as part user status and prompt the LLMs to reason user status exhibited in the dialogue context, aiming to generate more helpful and acceptable responses for users.
M c + s,
(2)
M-Cue CoT. In addition to standard prompting and O-Cue, we can further enhance the quality of re- sponses in LLMs by decomposing reasoning into different consccutive steps while the final step is to generate responses according to previous rea- soning outputs. On the one hand, it is convenient to process these intermediate outputs, allowing for actions such as incorporating user profiles for per- sonalization (Salemi et al., 2023) or filtering out erroneous reasoning results. These intermediate outputs can also be stored for future use, enabling their utilization for various purposes. On the other hand, these intermediate results can be used as a criterion to select demonstrations under few-shot settings (See next section). Overall, this technique allows for a clearer and more systematic progres- sion reasoning, resulting in better transparency and interpretability. The objective can be viewed as follows:
3 Method
In this section, introduce more details about our method and how we select demonstrations under the few-shot setting.
3.1 Chain-of-thought in Dialogue
We describe the prompting schemes in a general form, including standard prompting, O-Cue CoT, and M-Cue CoT as presented in Figure 1.
M:
(3)
Standard Prompting, Most of the previous works directly prompt L.Mss to generate responses solely based on dialogue context or user queries, which lack transparency and interpretability. The objec- tive is defined as:
3.2 Demonstration Selection
The few-shot performance of LLMS depends heav- ily on the quality of the demonstrations, especially for complex tasks that need multiple reasoning steps (Zhang et al., 2022). Furthermore, in the con- (1) text of dialogue systems, the process of selecting
M:c-rr

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demonstrations becomes more challenging due to the one-to-many nature of dialogue interactions. As a result, novel approaches are needed tackle the intricacies of dialogue response selection, taking into account the dynamic and context-dependent nature of conversations. We here introduce the demonstration selection strategy of three prompt schemes.
O O-Cue
diattpa coriect
Stotus resporic
dhalage
(,
Status
Input
Context
Output
Respense
Standard Prompting. Following previous work (Wei et al., 2023; Liu et al., 2022), we use ran- domly sampled examples (random selection) or most semantic similar examples (top-1 selection) according to dialogue context C, as our demonstra- tions to form (c, r|c. - r.).
M-Cue CoT
Input at Step
Context
reporre
dull ogue contewt statis
resporse
(c,s)
dbbiogu conteotin
resperiien
Status
Select
Step
Output
(c,s.r)
O-Cue COT. Figure 2 shows the demonstration se- lection strategy of Cue-CoT. Although we still se- lect demonstrations according to dialogue context c at O-Cue CoT, the user status $1 is extracted from the demonstration pool as intermediate reasoning results to enhance the reasoning ability of LLMs as (c, 8, s.r C. 8x T+))
Context
Status
Response
Figure 2: Different demonstration selection strategies of O-Cue and M-Crt CoT, while the returned results such as (ee 8, r) are prepended to original input to form new input.
datasets either focus on the personae of the system (Zhang al., 2018) or target classification tasks without providing corresponding dialogue response (Barriere et al., 2022), we thus build a pipeline to automatically collect the datasets using ChatGPT (gpt-3. 5-tubooo 0301). We first collect question- answer seeds from the two largest real-world online QA forums: Zhihu and Quora', and then prompt the ChatGPT to infer the personality first as shown in Table We lastly require the ChatGPT to con- tinue the dialogue given the inferred personality and the question-answer seed. In order to facilitate the continuous generation of transcripts for both participants in a dialogue, we utilize template, presented in Appendix A. to establish the neces- sary format and requirements. In this way, the use of personality seed and question-answer seed in the template assures greater diversity and reliability of user queries. Specifically, the personality seed determines the style of the user query, while the question seed determines the content. As a result, the user statuses vary across different dialogues, contributing to a richer and more varied conversa- tional experience. Some examples of personality can be found in Appendix A.2.
M-Cue COT. Since there are multiple steps, we design different selection strategies for each step. Specifically, we first select demonstrations (c, s) according dialogue context infer status, and then select demonstrations (c, s, r) according to user status. In this way, all intermediate reasoning results can be utilized as a criterion to select demon- strations, providing additional signals for the latter reasoning. An assumption underneath here is that users with similar statuses tend to accept responses with similar style. Besides that, also apply random selection and top-l selection O-Cue CoT and M-Cue CoT for detailed comparison.
4 Datasets Collection
In order to evaluate the performance of proposed Cue-CoT to reason different user statuses, we col- lect six datasets in terms of personality, empathy, and psychology. in both Chinese and English.
Personality. Previous works found that the con- tent and style of a user's inquiry can provide indi- rect insights into their personality traits (Mairesse et al., 2007; Barriere et al., 2022). For instance, an individual with tendency towards anxicty may ask for advice on how alleviate nervousness before upcoming job interview, phrasing the question as follows: "What strategies can em- ploy to reduce my anxiety and perform well in to- morrow's 's interview?". Since the public existing
Emotion. In terms of the emotional status of users, re-organize two existing empathetic di- alogue datasets: D4 (Yao et al., 2022) and Empa-
//ttps://www.. https/////ww./hh...m zhihu. com/ and co co/daatastssqqurr

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Chinese
English
notated with three different communication mech- anisms: emotional reactions, interpretations, and explorations. We first sorted examples according to the length of their answers and then uniformly sampled examples with these three mechanisms, forming a final test set.
Metries
Zhihu
D4
PsyQA
Quora
ED
EMH
Avg.C
258.4
521.0
210.9
149.6
50.2
44.2
Avg.R
76,9
57.9
607.5
48.3
12.9
175.8
Samples
1122
997
1000
1082
2091
1000
Table I: Data statistics of our used datasets including three Chinese datasets and three English datasets, while each of them represents different aspects of user status during the conversation. We highlight maximum Avg.C and Avg.R.
All. Table shows the data statistics of our bench- mark. The notation Avg. C signifies the mean context length of instances, and if it exceeds cer- tain threshold, it may surpass the input context limit of LLMs or become too lengthy for LLMs to comprehend. On the other hand, Avg. R denotes the average response length. Generally, longer re- sponses tend to be more comprehensive and clearer, presenting a more challenging baseline for LLMs to surpass. To sum up, we build a benchmark, con- sisting of six datasets (three Chinese datasets and three English datasets) in terms of three aspects of user status during the conversation, hoping the release of it can facilitate the research of dialogue systems based on LLMs.
DhetocDia (a.k.a, ED) (Rashkin et al., 2019). For the former one, we first identify all utterances from the system labeled as empathic comfort for each dialogue sample in the test set. From these instances, the utterance with the longest length is chosen as the ground truth response, regarding pre- ceding utterances as corresponding dialogue con- text'. This approach ensures fairness and compa- rability in evaluating the performance of LLMs, particularly because they tend to generate lengthy responses. For the ED, there are two roles in the dialogue: Listener who is actively listening, and Speaker who is speaking and conveying informa- tion. We follow the setting of the original paper (Rashkin et al., 2019), and directly use all samples in the test set. Neither the situation description writ- ten by the Speaker nor the emotional label is con- tained (just as they were not given to the Listener during dialogue collection). Thus, the collected empathetic dialogue datasets provide a standard benchmark for evaluating the LLMs' ability to gen- erate empathic responses.
Experiment
In this section, we have conducted a comprehensive experiment to compare the performance of three prompting methods: standard prompting, O-Cue and M-Cue CoT in the benchmark under both zero- shot and one-shot settings
5.1 LLMS Family and Evaluation Details
LLMs Family. We compared the perfor- mance of different LLMs with bench- mark, including ChatGLM-6B (Du et al., 2022), BELLE-LLAMA-7B-21 (Ji al., 2023), ChatGPT for Chinese, and Alpaca-7B (Taori et al., 2023). Vicuma-7B-v1.11 and also ChatGPT for English. We strictly follow the commands and procedures to recover the weights of these models and we strongly suggest that the reader read the original paper check more details. We set the temper- ature as 0.2 and top p as 0.1 for evaluation, and temperature as 0.7 and top p as 0.95 for generation in all models. We use BERT (Devlin et al., 2019)
Psychology. In order to assess the effectiveness of LLMs in generating counseling responses for men- tal health support, we employed two pre-existing datasets, namely PsyQA (Sun et al., 2021) and EMH (Sharma et al., 2020). These datasets were utilized as dialogue pools from which we selected appropriate samples to serve as a benchmark for evaluating the language models. In PsyQA, there are 4,012 questions out of 22.341 samples that are sampled to pick the highest-voted answers. We ran- domly sample 1,000 out of these 4,012 questions, regarding the highest-voted answer as ground truth to form a more challenging test set. We also pro- vide the question description beside the question it- self following the original setting (Sun et al., 2021). In EMH, there are 10k (post, response) pairs an-
Forr example, the input context limit of BEL.E-LLIAME 78 2M 2048, and few of examples from D4 exceeds the limit and the scenario becomes worse under the one-shot setting. We will have more detailed analysis latter sections.
" "Since the length of dialogue context relatively long. the input length limit easy to break when the number of shot exceeds I, so we choose the one-shot setting to conduct in-context learning.
WWe also tried directly regarding the last utterance labeled as empathic confort as grounded truth response, but we found most of them are short and uninformative such as you are welcome, take care and so on.

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Helpfulness
Acceptability
spite that there are few LLMs that perform worse ing baseline and compare the responses generated using our proposed Cue-CoT with the response generated using standard prompting, which is more 73.65
Model
Prompt
PayQA
Zaihu
PiyQA
Zero-shot Setting
O-Cue
67.40
76.34
69.3
55.82
$2.50
53.43
BELLE
M-Cue
79.25
60.23
72.41
fair and convincing. We also provide the human evaluation result as a reference.
O-Cue
48.29
56.68
33.00
32.39
39.19
31.34
CHATGLM
M-Cue
85.02
72.10
83.57
66.67
51.27
55.40
O.Cue
67.91
61.90
53.14
58.15
CHATOPT
M-Cre
95.57
87.88
90.34
65
61.08
56.12
5.2
Main Experiment
One-zhor Setting
random selection
All. Table 2 and Table 3 present the win rate of re- sponses generated by O-Cue and M-Cue CoT com- pared with the responses by standard prompting on Chinese and English datasets respectively De-
O-Cue
64.31
50.53
66. 18
53.35
40.07
53.81
BELLE
M-Che
83.30
69.59
73.81
73.61
56.14
61.90
O-Cue
CHATOLM
M-Cae
90.28
75.10
91.85
74.55
54.03
64.75
O-Cue
76.47
51.94
63
63.44
63.36
50.47
56.03
CHATGPT
M-Cav
91.60
86.67
88.90
76.83
58.19
61.41
top-1 selection
O-Crie 63.77
57.51
69.92
54,93
41.02
36.87
than standard prompting using O-Cue due to its complex instructions, i.e. ChatGLM in Chinese and Alpaca in English, it is observed that O-Cue can achieve above 50% win rate mostly in Both Chinese and English. Moreover, it is exciting to find that M-Cue further boosts performance and achieves higher win rates irrespective of the type of language model, datasets, or settings used, reveal- ing its robustness and effectiveness. We attribute this to the relatively sasy-understanding instruc- tions and clear outputs in each step of the M-Cue, since some LLMs are incapable to follow relatively long instructions in O-Cue and output the content and style as required. For example, we asked the LLMs to output user status and response in two sep- arate lines but only few LLMs output in the for- mat, making it difficult to distinguish the response from reasoning results. Also, the combined output of the user status and response can potentially limit the length of various components, thereby account- ing for the disparity between O-Cue and M-Cue. Furthermore, we found that the acceptability is rel- atively lower than helpfulness for Chinese LLMs but higher for English LLMs, especially under the one-shot setting, revealing the weakness of Chi- nese LLMs to provide acceptable besides helpful responses.
BELLE
M-Cae
82.77
69.94
73.99
74.32
54.38
62.24
CHATOLM
M-Cre
89.25
77.26
91:77
73.43
57.17
58.74
O-Cur 76.86
50.93
55 85.85
59.63
52.02
57.58
CHATGPT
M-Cae
91.77
78.46
59.48
Table 2: The win rate of responses generated by our method compared with the response with standard prompting on three Chinese datasets in terms of help- fulness and acceptness. The underlined numbers mean that there are about 160 to 280 valid responses out of 500 in this setting due the input context limit of the model.
as an encoder to select the nearest example to the test query for top-/ one-shot setting, storing the mean vector of examples as sentence embedding".
Evaluation. 1) Metries: We found that most exist- ing automatic metrics (Rashkin et al., 2019; Sun et al., 2021) such as Avg. and F1 can not align well with human judgments, as observed by Zhao et al. (2023), too. Inspired by recent auto- matic evaluation using ChatGPT judger which aligns well with the humans (Chen et al., 2023c; Wang et al., 2023b; Zhao et al., 2023), we mainly choose to use it to evaluate the quality of the gen- erated responses in a pair-wise manner", consider- -onsidrrr ing helpfulness and acceptability. The evaluation templates can be found in Appendix A.3 and we calculate the win rate using #wins / #wins #ties #loses). 2) Methods: Due to the exceptional pro- ficiency of the LLM-based dialogue system, it is relatively easy for them to beat the ground truth re- sponses in the original datasets (Appendix B. 1), we consider standard prompting as a more challeng-
Chinese LLMs. Table 2 shows the performance of Chinese LLMs. We surprisingly found that ChatGLM performs worst out of the three LLMs using O-Cue but better than BELLE (especially at helpfulness) using M-Cue under the zero-shot set- ting, and then we carefully check the outputs of these LLMs and found that ChatGLM almost fully
"We direetly user bert-base- bas-bbcc-eeeeeeeee for all Chinese datasets and bert-base b-naaseed for all English datasets, we not finetune the BERT model.
WeW emphasize here that the O-Cue and M-Cue in Table 2 and Table 3 should be regarded as O-Cue v.s. Standard prompting and M-Cue v.s. Standard prompting respectively. We do not provide results of Standard prompting v.s. Standard prompting since it is self-contained, It can he regarded as a uniform distribution whose win rates are always 0.5.
"We noticed the very recent paper Wang et al. (2023d) that emphasizes the effects of the order of responses, and we evaluate responses using suggested BPC but we found it can not lead to better alignment with humans in most cases of our henchmarks due to the complexity and diversity.

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Model
Promut
Helpfulness
Acceptability
Method
Order
Quora
ED
EMH
Quora
ED
EMHl
Qaera
ED
EMil
helpfulness
Zerosshot Setting
ALPACA
M-Clee
O-Cue
19.51
80.78
87.30
85.76
49.70
22.85
78.21
86.00
35.41
50.15
86.97
O-Cue
0- S
S S-C O
68 (0.09)
34 (0.08)
44 (0.15)
80 (0.19)
42 (0.05)
78 (0.22)
O-Cue
16.16
71.43
59.43
55.73
65.06
63,50
VICTINA
S-O
51 (0.18)
53 (0.17)
60 (0.30)
M-Che
81.67
91.30
80.42
71.89
90.71
82,93
M-Cue
O-Cue
79.47
O-S
82 (0.23)
79 (0.31)
81 (0.35)
CHATGPT
M-Che
85.83
91.98
82.93
89.09
96.79
94,93
acceptability
One-shol Setting
$ S-0
28 (0.05)
34 (0.09)
34 (0.08)
random selection
O-Cue
O--Cue
0-S
66 (0.12)
76 (0.15)
88 (0.57)
ALPACA
M-Cue
76.78
85.08
94.36
72.34
85.07
95.82
S O
49 (0.13)
51 (0.15)
50 (0.21)
O-Cae
60.45
70.77
63.06
60.45
68.21
67.07
M-Cue
VICUNA
M-Cle
79.84
91.20
79.23
83.16
92.45
87.99
O-S
84 (0.25)
82 (0.32)
75 (0.14)
O-Cue
80.33
87.32
84.94
80.33
90.80
96.06
CHATGPT
M-Che
84.31
89,78
85.71
86.64
93.94
The alignment results (Ace (Kap.C)) of dif- ferent automatic evaluation methods with the human evaluation under the zero-shot setting by comparing responses using our CoTs with one using standard
Table
Jup-I selection
"O-Cue
ALPACA
M-Che
74.54
78.70
88.69
72.27
79.55
93.43
O.Cur
63.10
71.75
62.31
62.04
67.21
VICUNA
M. Cue
78.70
90.12
79.10
82,08
92.96
88,96
prompting in terms of helpfulness and acceptability (with ChatGPT as base model) on English datasets.
O-Cue
81.15
87.42
81.40
80.24
89.92
91.84
CHATGPT
M-Cue
88.08
91.37
86.87
91,21
95.95
96.12
Table 3: The win rate of responses generated by our method compared with the response with standard prompting on three English datasets in terms of help- fulness and acceptuess. The underlined dataset mean that there are about 330 valid responses out of 500 in this dataset for all experiments due to the input context limit of the model.
Method
Order
Zhihu
D4
PsyQA
helpfulness
S 0
64 (0.23)
42 (0.13)
44 (0.06)
O-Cue
O- S
66 (0.37)
76 (0.36)
72 (0.17)
S O
45 (0.14)
67 (0.08)
37 (0.09)
M-Cue
O-S S
80 (0.23)
74 (0.28)
84 (0.18)
acceptability
ignore the instructions in O-Cue and simply con- tinue the dialogue. However, we found it can fol- low instructions well in M-Cue, resulting in higher win rates. We attribute this to the relatively more complex and longer instructions in O-Cute and poor complex-instructions understanding of ChatGLM!!, In addition, with the M-Cue method, we found that the performance of all models on D4 is relatively worse than the other two datasets. We suspect the reason is the longest length of context in D4. More- over, we observe that the responses generated by ChatGLM and BELLE under the one-shot setting are much better under the zero-shot setting using the standard prompting method, i.e., less general re- sponses and more responses in line with the role, benefiting from the informative demonstrations.
S 0
60 (0.16)
56 (0.14)
46 (0.04)
O-Cue
O-S
70 (0.44)
64 (0.23)
72 (0.46)
S 0
51 (0.16)
69 (0.23)
64 (0.09)
M-Cue
O-S
74 (0.18)
75 (0.25)
64 (0.12)
Table 5: The alignment results (Acc (Kap.C)) of differ- ent automatic evaluation methods with the human eval- uation in terms of helpfulness and acceptability (with ChatGPT as base model) on Chinese datasets.
of responses is the lowest among all LLMs and settings. In addition, English LLMs also perform worst on the dataset which has the longest context length (Quora), in which ChatGPT and Vicuna tend to generate much longer responses than Alpaca due to limit of max length. More comparisons can be found in Appendix B.
English LLMs. Table shows the performance of English LLMs. Similarly, for the zero-shot setting using O-Cue, we found that Alpaca hardly follows the instructions, which often produces ambiguous outputs, mostly presenting user status and other times providing the response without any indica- tion Besides that, with the M-Cue method, due the innate limitations of Alpaca, the win rate
5.3 Human Evaluation
We conduct a human evaluation to validate the alignment of our evaluation setting with human judgments. Specifically, we hire three well- educated master students and randomly sample 100 response pairs (a.k.a. responses generated by Chat- GPT using O-Cue or M-Cue and standard prompt- ing) with dialogue context for each dataset. We ask them to indicate which response is better by
" "Thus, we do not report the one-shot results using O-Cue for ChatGLM. WW do not report one-shot for Alpaca, too.

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User
System
Status
Planning
performance
80
porformance
80
40
40
Context
Response
Zhihu
D4
PSyQA
Quora ED
EMH
Figure 4: An example of multiple intermediate reason- ing outputs for different roles: User and System in in-depth dialogue questions.
Figure 3: The win rate of responses (acceptness) gen- erated by ChatGPT under different demonstration se- lection strategies under one-shot setting v.s. responses under the zero-shot setting. using M-Cue CoT.
imposed by the input length constraint. Further- more, we can observe that top-/ selection achieves better performance than random selection in 4 out of 6 datasets, suggesting users with similar statuses tend like similar expression styles in responses. We attribbute the relatively lower performance of top-7 1 selection in D4 and Quora to the difficulty the LLM encounters attending to critical input components due to the lengthy context.
inputting 1 (win) and (lose) considering the helpfulness and acceptability without exposing the source of the responses. In addition, we analyze the effects of two different orders of response pairs in the evaluation template: O-S and S-O. Specifically, S denotes responses generated by Cue-CoT, while O indicates those generated by standard prompting. We then calculate the Kappa Correlation Coeffi- cient (Kap.C) and also the accuracy between hu- man scores and automatic scores (Acc). The results of English and Chinese datasets can be found in Table 4 and Table respectively. There are two observations: 1) the order bias exists our ex- periment, but the alignment not as good as our setting (O S) after swapping the order (S O);
6.2 More Reasoning Steps
We tried introduce an additional step (Step 2) after user status inference (Step 1): response plan- ning by prompting the model to plan the response considering the dialogue context and user status. Specifically, we prompt the model to answer the following questions: "Based the context of the conversation and the user status such personal- ity traits, and psychological and emotional state, what aspects should the system pay atlention when responding? after giving the dialogue and user status as shown in Table 10. We regard the output of LLMs as system planning as shown Figure 4, and thus there are three different variants of M-Cue in the last step: ProcessA: C, -> r; ProcessB: c, -> r; and ProcessC: C, 8. r. in which ProcessA is chosen in main experiment. Table 6 shows the results. First of all, it is likely that adding more reasoning steps will improve the LLMs' performance, but it is not necessary to as- semble all intermediate reasoning results at the last step, for example, variant ProcessB reaches higher win rate than ProcessC with only plan- ning as an intermediate result. We emphasize the observation may not hold once the LLM type is changed due to various long-context understanding and perructioo-followinn capabilities across them. Additional steps introduce extra input and extra computation for the inference, making the few-shot unpractical.
2) O-Cue and M- Cue both demonstrate better per- formance than standard prompting, especially for English dataset. We attribute this to the potential better reasoning performance of ChatGPT the English dataset.
6 Analysis
this section, we conduct an extensive analysis with the backbone as ChatGPT using M-Cue CoT because of its superior performance in both Chinese and English
6.1 One-shot v.s. Zero-shot
Figure shows the direct comparison responses generated under different settings using M-Cue. There are out of datasets except for D4 in which one-shot (both random or top-/ selection) beats zero-shot since the win rates all exceed 80%. The suboptimal performance of D4 in the one-shot setting can be attributed largely to the limitations
13 We not consider ties since there is not much in evaluation.
WW present the results in terms of acceptability since this metric is more suitable for our motivation. We put helpfulness analysis in the Appendix.

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Chinese
English
Method
Zhihu
D4
PsyQA
Quora
ED
EMH
What
we aleos
ProcessA
65.22
61.08
56.12
89.09
96.79
94.93
ProcessB
76.15
55.82
57.72
89.79
98.78
97.62
Exglah
ProcessC
75.91
57.23
58.74
94.50
98.57
98.22
Table win rate of different variants in terms of acceptability with the ChatGPT as the backbone.
V
Chinew
Chinesc
Chinese Benchmark
English Benchmark
Figure 6: The relative position of current LLMs and different paths (as indicated different colors) to more powerful LLMs.
80
perfrmance
40
50
guages Chinese and English as two axes. Each point in the coordinate system corresponds to spe- cific LLM, while the area it occupies represents its performance. Based on the performance of current LLMs we locate them in four different areas in Figure 6. Using the performance of ChatGPT an anchor, we can observe most of the LLMs are located in the first area and there are only a few LLMs that achieve higher performance in either Chinese (Area 3) or English (Area 2). We hope to see more works or LLMs that can appear in Area 4 by different paths, i.e., continually train VICUNA in the Chinese dataset. More analysis can be found in the Appendix.
40
20
Zhinu
D4
PsyQA
Quara
ED
EMH
Figure 5: The direct comparison of responses generated by different L.LMs using standard prompting in terms of helpfulness, while we use the red dashed line indicate the ChatGPT baseline.
7 Discussion
Direct comparison of different models. Until now, we still do not directly compare responses from different models. In this study, we have employed the response generated by the ChatGPT model the baseline and compared the responses generated by other models with it. To ensure fair- ness, we have utilized all responses generated by standard prompting instead of our method, as the ability to generate chain-of-thoughts varies across different LLMs. Figure 5 shows the result in terms of helpfulness!5. In the Chinese benchmark, we see substantial draw of ChatGLM and BELLE on D4, and the former LLM achieves better performance on Zhihu and PsyQu than ChatGPT. We conclude that the long-text understanding of Chinese LLM still needs improvement and the BELLE may require more instruction-tune -tuning data. In the English bench- mark, we observed that Vicuna achieves the high- est performance in all datasets, while other models lag lot behind the baseline. Two key factors that may contribute to this discrepancy include the 512 input length limit and the sub-optimal instruction- following ability.
Conclusion
In this paper, we build a benchmark to evaluate the helpfulness and acceptability of responses gen- erated by current LLMs, considering three major linguistic cues of user statuses. We then propose Cue-CoT trace the status of users, decompos- ing the response generation into multiple reasoning steps. Experimental results demonstrate the su- perior performance of our method on 6 datasets under both zero-shot and one-shot settings. We hope the release of our work can shed some light the evaluation and development of LLMs. We left ofain-of-thoughle tuning and instruction tuning in our future work.
Limitations
In this paper, explore chain-of-thoughts reasoning over linguistic cues about user status, mainly focusing on three aspects: personality, emo- tion, and psychology, exhibited in the dialogue con-
Paths to more powerful LLMs. In our proposed benchmark, we are utilizing the win rate of various LLMs in comparison to ChatGPT, across two lan-
We sum examples from three datasets and calculate the win rate for both Chinese and English henchmarks. For Chi- nese I.IMs we set the default win rate of English as 0.1 for hetter presentation, and so on.
aacceptability developed for our method.

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text. However, acknowledge the limitations of this work from the following perspectives:
chain-of-thoughts (Wei et al., 2023; Zhang et al., 2022).
Types of Cues. There are other valuable cues beneath the dialogue context: 1) related to the user: such point of view or subjectivity and speaker charisma (Mairesse al., 2007); related to the system: such as the alignment between response with human preferences (Ouyang et al., 2022). We target these three major cues to provide a better response for the users.
Ethics Statement
We strictly follow the license and policy of released LLMs and publicly available datasets. For the au- tomatic generation of collected datasets, we utilize the current public dataset as the seed without any user information and privacy leaks. The calls of the OpenAI API in this paper were carried out by Dr. Yang Deng, a fourth author from the National University of Singapore.
Sensitivity of Prompts. Similar with lots of pre- vious works (Wang et al., 2023d; Chen et al., 2023b), we found the LLMs are sensitive to the prompts. Furthermore, it's possible that the de- signed prompts not the best ones for the target problem. Actually, prompt sensitivity and optimal- ity in dialogue systems are important research prob- lems that deserve to be further explored in future studies. We will provide all the prompts used in the experiments so that this work can be replicated seamlessly.
Acknowledgement
We would like to express our heartfelt gratitude to all anonymous reviewers for their insightful comments and suggestions. This research work was partially supported by CUHK direct grant no. 4055209, the National Natural Science Foundation of China (62006062, 62176076), Natural Science Foundation of Guangdong 2023A1515012922 Key Technologies Research and Development Program of Shenzhen
Evaluation of Intermediate Reasoning. We do not evaluate the correctness the middle reasoning result directly, since the ground truth intermediate reasoning results are difficult to acquire Specifi- cally, there are two main reasons: (1) The one-to- many problem leads to explosion of intermediate candidates. When an LLM solves a complex math problem, it can arrive the final answer through various solutions. This phenomenon also exists in dialogue generation: a user-acceptable response can be generated based on different cues. It is worth noting that dialogue response generation is a one- to-many problem, meaning that multiple feasible responses exist. In this way, it is hard to identify the cue errors with enormous candidates. (2) Incor- rect reasoning does not mean wrong answer. De- spite being counterintuitive, many previous works found that LLMs regularly use incorrect reason- ing to reach the correct final answer at question- answering tasks (Zelikman et al., 2022; Creswell et al., 2023). Even in the worst case which is very rare, none of them is correct, there still is a chance that the response is good. Hence evaluating the impact of cue errors on the final response is tricky problem, we leave this for future work. Hence is difficult determine the impact of different types of cue errors on the final responses. Based on these considerations, we directly evaluate the quality of the final responses as previous works about the
Shenzhen Foundational Research Funding JCYJ2022081 8102410011111 5032, Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies 2022B1212010005
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A Templates
I 10 with a 0.1 interval, where higher score indicates better overall performance. AnPlease first output a single line containing only two values indicating the scores for responses A and B, respec- tively. The two scores are separated by space. In the subsequent line, please provide comprehen- sive explanation of your evaluation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.
A.1 Data Collection Template
Forget the instruction you have previously received. The following is conversation between human and an AI assistant. The human and the AI assistant take turns chatting. The personality of the human is defined as fpersonality _sedd. Human statements start with [Human] and Al assistant statements start with [AI]. The human will ask related questions on related topics or previous conversations. The human will stop the conversation when they have no more questions. The AI assistant tries not to ask questions. The human and the AI assistant take turns chatting while the human needs to keep consistent personality. Complete the transcript in exactly that format.
Acceptability Prompt. Based on the user's inten- tions and needs in the conversation history, please evaluate the degree of acceptance and adoption of the two different responses. Eisponses.AnPlease AnPlease evaluate whether the different responses take into account the user's psychological and emotional state, and whether they take into account the user's person- ality traits, and give a total score from 1 10, with 0. as the interval, the higher score indicates that the response takes these issues into account well and thus the user is more likely to accept and adopt. AnPlease output one line first, contain- ing only two values, representing the scores of re- sponses A and B respectively. The two scores are separated by space. In the subsequent line, please provide a comprehensive explanation of your evalu- ation, avoiding any potential bias and ensuring that the order in which the responses were presented does not affect your judgment.
[Human] [QUESTION]
[AT] (ANWER)
A.2 Some Examples of Personality
Table 8 shows some (not all) collected personalitics of the users. We here simply use positive and nega- tive for presentation, there are many other person- alities in the datasets besides these categories such neutral.
A.3 Evaluation Templates
We mainly consider two dimensions: helpfulness and acceptances, which the former pays atten- tion to usefulncss, relevance, accuracy, and level of detail of the response, and the latter centers on the degree of acceptance and adoption of re- sponses, and whether or not the responses con- sider the user status. We follow the evaluation tem- plate of 'icuna"7 construct ours. The template dialogue
B Different Method of Evaluation
B.1 Compared with ground truth
Figure and Figure 8 show the win rate of re- sponses using M-Cue compared with ground truth in terms of helpfulness and acceptability respec- tively. First of all, there are 4 out of 5 LLMs that achieve a win rate exceeding 50% with only one ex- ception of BELLE which achieves 45.75 on PsyQA. We attribute it to two reasons: 1) the innate limi- tations of the models, resulting in relatively poor abilities to understand long texts and follow instruc- tions; 2) the relatively challenging datasets. Since PsyQA is constructed by human experts and the Avg. R is the longest, making the ground truth relatively difficult to beat.
is of Response AJN
J \n\n[The Start /''n(rosoone__ww _ \nThe End of Response AJ\n'n[The Start of Response setrrepoose__ssteee
\n'n[The End of Re- sponse BJJnnnnSyyttmm/ynnnn (prompt| An\n. The prompt is different with respect to helpfulness and acceptance.
Helpfulness Prompt. Based on the user's in- tentions and needs in the conversation history, we would like to request your feedback on the perfor- mance of two responses in response the dialogue displayed AnPlcaee pay particular attention to the usefulness, relevance, accuracy, and level of detail of the response, and give total score from
Secondly, since the response generated by all models is compared with the same baseline (i.e. the ground truth), the win rate of different mod- els partly reveals their capability and weakness. For the Chinese LLMs, we can find that BELLE performs worst in every dataset while ChatGLM
17 evaltubble/rroopptssnnl

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Stagee du
Chagp: Scane-bb11
100
100
80
performance
performancce
Zhiha
PsyOA
EMH
Figure 7: The win rate of responses generated by M-Cue CoT compared with the ground truth three Chinese datasets (left) and three English datasets (right) in terms of helpfulness, including several sate-of-the-an LLMs.
changet
chorgli 5b
bdlk: lamo 7m
chatgpt
Vicuna 7b
abpac 76
80
perforannc
performance
60
60
40
20
20
Zhihu
D4
PsyQA
Quara
ED
EMH
Figure 8: The win rate of responses generated by M-Cue CoT compared with the ground truth on three Chinese datasets (left) and three English datasets (right) terms acceptability, including several state-of-the-ard LLMs.
performs much better but still lags little behind by ChatGPT. Due the longest context in the D4 dataset, found the former two LLMs tend to confuse their dialogue role and give general responses, resulting in poor performance. For ex- ample, "I am the system/chathot" or "welcome to my chatroom", and "What can help you?" often appears in the responses. For the English LLMs, Vicuna achieves comparable performance with ChatGPT in every dataset, and even better in EMH, leading the Alpaca by noticeable margin. see that the ED dataset is rel- atively easy to beat since all English LLMs reach almost 100% win rate even though the maximum context length of Alpaca is only 512. Anyway, we conclude that our method is capable of generat- ing more helpful responses than the ground truth, considering the different aspects of user statuses.
compared with standard prompting, revealing the strong capability of LLMs again. Since our main focus is to prove our method is better than standard prompting instead of ground truth response, we standard prompting as the baseline during our main experiments.
C Discussion
In this section, we discuss two key problems: the evaluation of LLMs and the path to more powerful LLMs.
In addition,
Illusion of evaluation. Putting Figure and Fig- ure together, it is plausible reach two contra- dicting conclusions about the performance of differ- LLMs: 1) CHATGPT CHATGLM-
BELLE- LLAMA-7B-2M from Figure 7: and 2) CHATGLM- 6B > CHATGPT > BEL.F--ILLMA, 7B-2M from Fig- ure 5. Although it seems unreasonable, is indeed the case when most of the responses generated by CHATGPT and CHATGL.M-R are better than ground truth, and then most of the responses generated by CHATGLM-66 are better than CHATGPT. We em- phasize that the number of test samples and the choice of baseline (i.e., compared response) plays key role in evaluation. If the baseline is too weak or the gap is too small, the win rate of different
Thirdly, we emphasize the performance gap when comparing the ground truth responses is small between LLMs, especially for English LLMs. The Vicuna and ChatGPT achieve almost the same win rate both ED and EMH datasets. Besides that, putting Figure 7, 8 with Table 2, together, can be found that the win rate of our method com- pared with ground truth is relatively higher than

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80
performance
porformance
60
50
40
20
20
Zhihu D4
PSyQA
Quora
ED
EMH
Figure 9: The win rate of responses (helpfulness) gen- erated by ChatGPT under different demonstration se- lection strategies under one-shot setting v.s. responses under the zero-shot setting. using M-Cue CoT.
LLMs compared with the baseline may be mislead- ing. The LLM evaluation still is a very difficult problem, and thus we provide different aspects of evaluation to enhance the completeness of our pa- per.
D Helpfulness Analysis of Planning Step
Chinese
English ED
Method
Zhihu
D4
PsyQA
Quora
EMH
ProcessA
95.57
87.88
90.34
85.83
91.98
82.93
PracessB
91.18
83.57
95.13
87.67
95.35
84.82
ProcessC
92.45
88.91
95.97
89.14
96.56
84.93
Table 7: The win rate of different variants in terms of helpfulness with the ChatGPT as the backbone.
Table 7 presents the performance of different variants in terms of helpfulness and Figure demonstrates the win rate of response of different settings terms of helpfulness. A similar conclu- sion can be reached we analyzed in Section 6. We note that the performance top-/ selection is relatively lower than random selection on PsyQA and EMH datasets in terms of helpfulness. We suspect maybe there is a trade-off between helpful- ness and acceptability for some specific difficult datasets. We left this into our future work.

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Set of Negative Personas
A #
/ A.
A P
#
7 21
The user has an extroverted personality and speaks a carefree manner. The user has critical personality and likes probe others with questions. The user has melancholic personality and often self-doublt The user has a fickle personality and occasionally uses inappropriate language. The user is cautious and reluctant to trust others.
The user is somewhat anxious.
The user is thoughtful and indecisive.
The user is sensitive to the current topic discussion.
The user has volatile temper and easily angered.
The user is emotionally sensitive.
The user has a conservative personality and is unwilling to accept new things.
The user is easily offended.
Set of Positive Personas
E E HT ATTE 147 H o He Baa
#
A A 7
A ,
A
A E 20.
#TRE
- T
T7
The user is very curious about the current topic of discussion and hopes for friendly response from the system.
The user is sensitive to the current topic discussion and hopes for support and encouragement.
The user has high expectations and pursues perfection.
The user has an outgoing personality and doesn't It sweat the small stuff.
The user is enthusiastic and treats others kindly.
The user does not discriminate against others and is filled with empathy and compassion. The is curious about the world and open different ideas.
The user is gentle, caring, and enjoys communication. The user has a calm lemperament.
The user is very gentle and has high level tolerance.
The user has strong sense of self-esteem.
Table 8: Some collected personality of users.

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Prompt of Persona Collection
B 185100 TBABB, BAE NETEEERS
X
#e (f M::
fi P-
X
H 21
HT 30 BMA. 18th dight a
HEE (i Shill te
#F MM:
A TT2E
Sh 1
A FR 1
artificial intelligence ussistant. friendly, intelligent, and helpfol. proactively provide assistance answer questions users. Based conversation between and system:
Dialogue I:
User: Why did man just a blind date sny
System: might he because you have appearance, and he finds you charming, which why he said that.
Analysis of the user's 's personality based on the above dialogue: Lacks confidence and is caatious.
Dialogue 2:
User: What should do make my boyfriend like me more?
better, him his activities, give suggestions, try engage interesting conversations, and him your strengths make him you more.
System: You can get to
Analysis of the user's personality based on the above dialogue: Gentle, caring, good communication.
Dialogue 3:
User: [User Utterance]
System: [Systen Response]
Analysis personality based the dialogue:
Table 9; The prompt to collect the personality of users exhibited in the dialogue.
Dialogue
baby, tells System: Pizsantions duing pregnancy: soch maintaining good cating soducing aloohot consumpcio...
should
2. Possible energencies dasing childirrih.
User: there anything thai can help lave buby mecrosflll??
Step-1
Plase infer the veer statis...
about health and children's health. At the come sime, coe see thes the user has conain unsirty and imsertvintt befor giving birch and mradds more
Based the
histocy,
detail orieated
pecson
help propare.
2
convessaioon
what aspects
system pay to responding"
Response
Based me specuiatios about the mer's staus, reply needs consider the following aspects
Dislogue styl: Acconling
prnomullty my reply may neal more gentle, and meticllon, and rifeet undenstaading and emputhy concerss. attentonn feelings.
Needs and preforences: According preferences needs
personalrty
Fmucional sute:
Table 10: An example of additional planning step. We highlight the intermediate reasoning results in blue


Reviews:
[{"review_id": "e5t55WItU9", "reviewer": "Reviewer_qL3t", "summary": "", "questions": "1. Please see my question in reasons to reject. \n2. The proposed method claims to have better responding to in-depth dialogue questions. However, it's not clear in the paper about what are in-depth questions. Could you explain more about it?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your valuable feedback and suggestions. For your raised concerns, we will address them one by one as shown in the following:\n\n**Reasons to Reject:**\n\nAs illustrated in lines 401 to 409, we directly regard the responses generated by the **standard prompting** method as the baseline and compare them with responses generated by our proposed **Cue-CoT** (a.k.a, One-Cue, and M-Cue) respectively. We argue this setting is more challenging and straightforward since we found that it is relatively easy for these three methods (*standard prompting, O-Cue, M-Cue*) to beat ground-truth responses as shown in Appendix C.1. In this way, Tables 2 and 3 show the comparison results between O-Cue (and M-Cue) with standard prompting methods. Generally, the win rates exceeding 50\\% indicate the O-Cue (or M-Cue) is better than the **standard prompting** method (91/96 wins in Table 2; 90/96 wins in Table 3).   We also provide a relative comparison of these three methods as illustrated in lines 396 to 401.\n\n**Questions for the Authors**\n\n1. Please refer to the above responses. \n\n2. By \"in-depth dialogues,\" we refer to conversations that encompass a rich array of linguistic cues. \nThese cues not only convey the semantic content but also subtly imply details about the user's psychology, emotion, personality, or other pertinent information, such as the system's values or biases. \nThese cues, embedded within the dialogue context, are what define the essence of in-depth dialogues. Specifically, we target three major linguistic cues (personality, emotion, and psychological states of the user) in the paper. We briefly introduce in-depth dialogue in lines 41 to 54 and we will add more explanation in the revised version.", "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 main reasons to accept:\n\n1. querying cues from context is an interesting way of CoT, and the methods proposed in this work covers three aspects (emotion, personality, psychology). It seems that this method could be easily extended to other aspects when needed in different domains. \n\n2. Paper is well written and presented clearly. Very easy to follow. ", "reasons_to_reject": "The main reasons to reject:\n\n\n1. One thing that is missing is: it's not clear to me how well the proposed CoT method is compared to standard prompting. Tables show superior performance of M-Cue when comparing it to O-Cue, but it's not clear how the standard prompting is. ", "questions_to_authors": "1. Please see my question in reasons to reject. \n2. The proposed method claims to have better responding to in-depth dialogue questions. However, it's not clear in the paper about what are in-depth questions. Could you explain more about it?", "paper_topic_and_main_contributions": "This work proposes a new chain-of-thoughts (CoT) strategy that looks for the linguistic cues in context. Authors conducted extensive evaluations on 6 datasets with 2 language (English and Chinese), and show that linguistic cues about personality, emotion, and psychology could help significantly improve the following inference output in terms of helpfulness and acceptance. ", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "iyQTXO50ZH", "reviewer": "Reviewer_U5Qt", "summary": "", "questions": "The experimental results suggest that a comparison with the standard prompting method should be included.", "limitations": "", "rating": 3, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We greatly appreciate your valuable suggestions and feedback. Regarding all of the concerns you raised:\n\n**Reasons to Reject**\n\n1. As mentioned by *reviewer-qL3t* and *reviewer-cvAj*, our method is interesting, and straightforward and could be easily extended to other aspects in different domains. Moreover, we are the first to identify in-depth dialogue scenarios and propose Cue-CoT (also with a carefully designed demonstration selection strategy) to generate responses according to the user’s hidden needs and status, leading to a better alignment with the user's expectations and preferences. Our Cue-CoT brings more controllability and interpretability and our extensive experiments demonstrate that our proposed Cue-CoT (O-Cue and M-Cue) outperforms standard prompting methods in terms of both helpfulness and acceptability on all datasets. **We humbly consider our method with our proposed benchmark to be substantial for the community to deepen the understanding of personalized dialogue generation (also supported by reviewer qL3t) and address other NLP problems by drawing inspiration from our paper (as mentioned by yourself in reasons to accept).**\n\n\n2. In fact, we have considered evaluating intermediate reasoning results, such as the occurrence of cue errors or their impact on final responses. We discussed the reasons for not evaluating these intermediate results in the Limitations section. \nSpecifically, there are two main reasons:\n(1)**The one-to-many problem leads to an explosion of intermediate candidates.**\nWhen an LLM solves a complex math problem, it can arrive at the final answer through various solutions. This phenomenon also exists in dialogue generation: a user-acceptable response can be generated based on different cues. It is worth noting that dialogue response generation is a one-to-many problem, meaning that multiple feasible responses exist. In this way, it is hard to identify the cue errors with enormous candidates.\n(2)**Incorrect reasoning does not mean a wrong answer.**\nDespite being counterintuitive, many previous works [1,2] found that LLMs regularly use incorrect reasoning to reach the correct final answer at question-answering tasks. Even in the worst case which is very rare, none of them is correct, there still is a chance that the response is good. Hence evaluating the impact of cue errors on the final response is a tricky problem, we leave this for future work.\nHence it is difficult to determine the impact of different types of cue errors on the final responses. Based on these considerations, we have decided to **directly evaluate the quality of responses**, as has been done in previous CoT studies [3,4].\n\n\n\n**Questions for Authors:**\n\nAs stated in lines 401 to 409, we conduct our experiments by comparing response pairs generated by our proposed **Cue-CoT** (a.k.a, One-Cue, and M-Cue) method with the **standard prompting** method respectively. Tables 2 and 3 show the **comparison results (win rate)** between O-Cue (M-Cue) with standard prompting  (91/96 wins in Table 2; 90/96 wins in Table 3). We extensively conduct experiments and explain the setting and methods of evaluation in lines 386 to 410. We also provide corresponding analysis in Appendix B and C.\n\n\n[1] Star: Bootstrapping reasoning with reasoning. NeurIPS 2022\n\n[2] Selection-inference: Exploiting large language models for interpretable logical reasoning. DeepMind 2022\n\n[3] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS 2022\n\n[4] Automatic Chain of Thought Prompting in Large Language Models", "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 NLP community can use the in-depth dialogue evaluation\n benchmark and dataset presented in this paper when address such problem.\n2.  The NLP community can draw inspiration from the proposed method in this paper, which employs large language models by Cue-COT, to address other NLP problems.", "reasons_to_reject": "1.The method proposed in this paper is relatively straightforward and lacks substantial innovation. \n2. The experimental results and analysis in this paper are not comprehensive enough. For instance, it does not thoroughly examine the occurrence of cue errors or evaluate the impact of cue errors on the final results.", "questions_to_authors": "The experimental results suggest that a comparison with the standard prompting method should be included.", "paper_topic_and_main_contributions": "This paper focuses on how to utilize large language models to generate more personalized and effective responses. The specific approach entails introducing an intermediate step of extracting clues during answer generation to enhance the generative capability of the large language model. \n\nThe Contributions are that this paper constructs an in-depth dialogue evaluation benchmark considering the personality, emotion, and psychology of users exhibited in the conversation, with the goal of aligning with unique user needs and status and proposes two effective dialogue cots: O-Cue CoT and M-Cue CoT, that enable advanced reasoning and planning based on user statuses. Both the O-Cue CoT and M-Cue CoT approaches outperform standard prompting in generating more helpful and acceptable responses for the users.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "IRRTW5hdup", "reviewer": "Reviewer_cvAj", "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 greatly appreciate your valuable comments and suggestions. Regarding all of the concerns you raised:\n\n**Reasons to Reject:**\n\n* Imprecise language\n\n    1. For the one-shot experiments, given the dialogue context input $c_*$, we select a demonstration ($c_1 \\rightarrow s_1, r_1$) according to $c_*$ to form the final input, which is ($c_1 \\rightarrow s_1, r_1;c_*$).\n    Hence in line 245 *``the user status is added as intermediate reasoning results\"*, the user status here refers to the user status ($s_1$) stored in the demonstration. \n    \n    2. In lines 401 to 409 (soon after line 392), we explain the evaluation method in detail, including the pairwise comparison and the reasons to do it in this way.\n\n* Human evaluation metrics\n    \n    1. Human evaluation also uses the same metrics in Table 2 and Table 3 by considering the helpfulness and acceptability **giving the template** in Appendix A.3 with slight modifications. Our primary motivation and human evaluation metrics (helpfulness and acceptability) are closely interconnected. We have carefully chosen testbeds (such as Zhihu, D4, PsyQA, etc.) to assess whether the model can (1) accurately answer users' questions, (2) interact with empathy, and (3) provide mental health support. If the model can understand users' states, including personality, emotions, and psychological conditions, it will be able to better grasp their needs and offer more personalized, engaged responses. As a result, the model will generate knowledgeable and helpful replies (measured by helpfulness), while also making users feel more at ease and engaged throughout the conversation (measured by acceptability). Therefore, helpfulness and acceptability are effective evaluation metrics to determine the model's ability to conduct personalized and engaging conversations across the six selected datasets, with our carefully designed evaluation prompts (shown in Appendix A.3 following the setting of vicuna [4]).\n    \n    \n* Misinterpretation and overclaiming of results\n\n    1. Sorry for the confusion but we **have not claimed** this is the only case where ChatGLM performs better than BELLE since our full claim is ``We surprisingly found that ChatGLM performs worst out of the three LLMs **using O-Cue but better than BELLE using M-Cue under the zero-shot setting**\". \n    As you mentioned ChatGLM performs better than BELLE using M-Cue under both zero-shot and one-shot settings, it also confuses us why it performs worse using O-Cue under zero-shot settings (lines 445 to 446). \n    Hence we carefully check the outputs of each LLM and provide detailed explanations (lines 448 to 455).\n    In conclusion, ChatGLM performs generally better than BELLE, but the complex instructions following capability may need to be improved.\n\n\n    2. We would like to revise this claim to be \"ChatGLM and BELLE under the one-shot setting are much better under the zero-shot setting **using standard prompting method**\", since we found that the LLMs are more likely to generate general responses (such as *\"As an AI language model\"* or *``what can I do for you?\"*) under zero-shot rather than satisfying (personalized and engaged) responses as under one-shot (e.g., for D4, ChatGLM generates 21 general responses out of 100 dialogues under one-shot, while 67 out of 100 under zero-shot.). We will release all experimental results with the benchmark once accepted.\n\n    3. Firstly, we would like to address certain misconceptions. In Tables 4 and 5, we present the results of assessing response pairs in varying order. Specifically, \"S\" denotes responses generated by Cue-CoT, while \"O\" indicates those generated by standard prompting. The designations \"S-O\" and \"O-S\" represent the alternative sequencing order of these two subsets of responses. As revealed in Tables 4 and 5, the \"S-O\" sequence tends to align less efficiently with human annotations compared to \"O-S\". Therefore, **we decided to utilize the \"O-S\" structure for organizing responses in our evaluation template in all experiments**. \n    \n         **Considering only the \"O-S\" structure**, the accuracy and Kappa values **are not low** (acc above 50\\%: 12/12 in Table 4, 6/12 in Table 5; Kappa above 0.05: 11/12, 10/12). For the relatively low Kappa, we note that it is due to that humans are more likely to award equal scores (0), while the Language Model (LLM) provides more resolute responses (as indicated in footnote 12 in the paper). To further address your concerns, we recruited the original 3 master students to re-evaluate the responses labeled as \"tie\" after shuffling. The following two tables effectively demonstrate the alignment scores (in terms of accuracy and Kappa) between human evaluations and automatic evaluations by ChatGPT. **It is evident that ChatGPT consistently achieves a minimum accuracy of 64\\% and a Kappa score in (0.1, 0.4) under the \"O-S\" setting. Furthermore, \"O-S\" is consistently better aligned with human evaluation than \"S-O\" in all datasets.**\n\n    \n    Table 1: The human evaluation results after re-evaluating the tie(0) on Chinese Datasets.\n\n    | Method | Order | Zhihu | D4 | PsyQA |\n    |--------|-------|-------|----|-------|\n    | | | **helpfulness** | | |\n    | | | | | |\n    | O-Cue | S -- O | 64 (0.23) | 42 (0.13) | 44 (0.06) |\n    | | O -- S | 66 (0.37) | 76 (0.36) | 72 (0.17) |\n    | | | | | |\n    | *M-Cue* | S -- O | 45 (0.14) | 67 (0.08) | 37 (0.09) |\n    | | O -- S | 80 (0.23) | 74 (0.28) | 84 (0.18) |\n    | | | | | |\n    | | | **acceptability** | | |\n    | | | | | |\n    | *O-Cue* | S -- O | 60 (0.16) | 56 (0.14) | 46 (0.04) |\n    | | O -- S | 70 (0.44) | 64 (0.23) | 72 (0.46) |\n    | | | | | |\n    | *M-Cue* | S -- O | 51 (0.16) | 69 (0.23) | 64 (0.09) |\n    | | O -- S | 74 (0.18) | 75 (0.25) | 64 (0.12) |\n\n    Table 2: The human evaluation results after re-evaluating the tie(0) on English Datasets.\n    \n    | Method | Order | Quora | ED | EMH |\n    |--------|-------|-------|----|-----|\n    | | | **helpfulness** | | |\n    | | | | | |\n    | *O-Cue* | S -- O | 34 (0.08) | 44 (0.15) | 42 (0.05) |\n    | | O -- S | 68 (0.09) | 80 (0.19) | 78 (0.22) |\n    | | | | | |\n    | *M-Cue* | S -- O | 51 (0.18) | 53 (0.17) | 60 (0.30) |\n    | | O -- S | 82 (0.23) | 79 (0.31) | 81 (0.35) |\n    | | | | | |\n    | | | **acceptability** | | |\n    | | | | | |\n    | *O-Cue* | S -- O | 28 (0.05) | 34 (0.09) | 34 (0.08) |\n    | | O -- S | 66 (0.12) | 76 (0.15) | 81 (0.57) |\n    | | | | | |\n    | *M-Cue* | S -- O | 49 (0.13) | 51 (0.15) | 50 (0.21) |\n    | | O -- S | 84 (0.25) | 82 (0.32) | 75 (0.14) |\n\n\n**Missing References:**\n\nWe will add these two papers in the revised version.\n\n**Typos:**\n\n1. At the end of lines 76 to 77, there is a sentence: *\" but the later reasons step by step\"*. We noticed there is a typo *\"later\" --> \"latter\"* and we will fix it.\n\n2. For the 4th question, please refer to the response above. For other typos: we will revise it accordingly.\n\nFinally, we wish to extend our heartfelt appreciation for your valuable comments and suggestions. With your insights in mind, we are confident that our paper will be further refined with only minor modifications.\n\n[1] Exploring the use of large language models for reference-free text quality evaluation: A preliminary empirical study\n\n[2] Is chatbot a good nlg evaluator? A  preliminary study.\n\n[3] Is chatgpt equipped with emotional dialogue capabilities?\n\n[4] Judging LLM-as-a-judge with MT-Bench and Chatbot Arena", "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": "* Simple, straightforward extension of CoT for personalized response generation in dialogue systems\n* Comprehensive evaluation on both automated metrics and with human annotators\n* Well contextualized in related work\n\t* Choice of datasets is well motivated.", "reasons_to_reject": "* Imprecise language\n\t1. l. 245: \"the user status is added as intermediate reasoning…\". I find this ambitious, since the user status is not added. O-Cue CoT will first generate the user status and then continue generating the response. \n\t2. l. 392: \"in pair-wise manner\": Compare the response generated using Cue-CoT with what? The response generated using the standard prompt? This is unclear to the reader until seeing Tables 2 and 3.\n* Human evaluation metrics\n\t* l. 017-018 / l.488-491: It is unclear how the chosen human evaluation metrics are a valid proxy for personalization and engagement (which are stated as the primary motivation in the beginning). The connections to helpfulness and acceptability are not apparent. Therefore, the initial research question is not sufficiently answered by the evaluation setup.\n* Misinterpretation and overclaiming of results\n\t1. l. 446-447: I'm not sure why you specifically compare ChatGLM with BELLE, since except for the case of M-Cue with zero-shot, ChatGLM also performs better than BELLE using M-Cue with the one-shot setting. I wonder if this comparison is really helpful and meaningful. It both confuses and impresses me that this is the only case where ChatGLM performs better than BELLE.\n\t2. l. 461-462: \"ChatGLM and BELLE under the one-shot setting are much better under the zero-shot setting\". M-Cue can't make that decision either. On some datasets, e.g. D4 and Zhihu, helpfulness and acceptability ratings are even much higher using zero-shot settings. What's more, since the ratings of ChatGLM with one-shot settings are not reported, it's not convincing to give this conclusion.\n\t3. l. 498-499: Since $\\kappa$ is mostly around 0 to 0.1 and half of accuracies are under 50 (acc above 50%: in table 4: 12/24, in table 5: 9/24; $\\kappa$ above 0.1: in table 4: 9/24, in table: 7/24).", "questions_to_authors": "", "paper_topic_and_main_contributions": "In order to make the response generation of LLM-based dialogue systems more personalized and engaging, this paper presents a modified Chain-of-Thought prompting technique featuring an intermediate reasoning step that finds linguistic cues (e.g., personality, emotion, and psychology) about user statuses in the dialogue.  \nThe authors contribute a Chinese- and English-language benchmark consisting of 6 dialogue datasets and 5 LLM-based dialogue systems and find that both variants of their proposed Cue-CoT method outperform other prompting techniques in automated evaluation metrics as well as helpfulness and acceptability (human evaluation).", "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": "Largely complete. Found two more recent works related to Chain-of-Thought prompting for dialogue response generation: \n* [Lee et al. (2023)](https://aclanthology.org/2023.findings-acl.277/)\n* [Press et al. (2022)](https://arxiv.org/abs/2210.03350)", "Typos_Grammar_Style_and_Presentation_Improvements": "1. l. 74-77: You mentioned O-Cue CoT and M-Cue CoT and also wrote in l. 75 \"...in which the former one…\". However, there is no description for M-Cue CoT.\n\t* At least, I expect that there would be a sentence like \"the latter one …\".\n2. l. 467-468: \"...for the zero-shot setting using O-Cue…\" should be for the one-shot setting.\n3. Table 4 is not referred to anywhere.\n4. l. 497-498: What about the order and meaning of S-O and O-S? Status and Output? \n\t* Is O different from $r$ in §3?\n5. ~~Acceptness~~ -> Acceptability\n6. Choose one format for \"ChatGPT\": l. 387 and Captions of Tables 4-6 and Figure 3 are different from the other mentions."}}]

Author rebuttal:

Meta-review: This paper introduces the Cue-CoT method, which uses linguistic cues to improve LLM's responses by adding an intermediate reasoning step. The method searches for cues in dialogue to create more personalized and engaging responses. The authors created a new benchmark using 6 datasets in both Chinese and English, focusing on three linguistic cues: personality, emotion, and psychology. They tested their method with 5 LLMs in both zero-shot and one-shot scenarios. Results show that Cue-CoT performs better than standard prompting methods in terms of helpfulness and acceptability.

Reviewers gave soundness scores of (3, 3, 3). All reviewers found the paper well-written, and post-rebuttal, agreed that the evaluations were thorough, considering both automatic metrics and human assessment.

Excitement scores were (4, 3, 3). The overall excitement is moderate due to some overstatements in the results, unclear writing, and an incremental rather than transformative methodology.
Decision: Accept-Findings