Evaluating Intention Detection Capability of Large Language Models in Persuasive Dialogues (2024)

Abstract: We investigate intention detection in persuasive multi-turn dialogs employing the largest available Large Language Models (LLMs).Much of the prior research measures the intention detection capability of machine learning models without considering the conversational history.To evaluate LLMs’ intention detection capability in conversation, we modified the existing datasets of persuasive conversation and created datasets using a multiple-choice paradigm.It is crucial to consider others’ perspectives through their utterances when engaging in a persuasive conversation, especially when making a request or reply that is inconvenient for others.This feature makes the persuasive dialogue suitable for the dataset of measuring intention detection capability.We incorporate the concept of ‘face acts,’ which categorize how utterances affect mental states.This approach enables us to measure intention detection capability by focusing on crucial intentions and to conduct comprehensible analysis according to intention types.

## introduction identifying the speaker's intention is crucial for maintaining a smooth conversation. suppose a situation where alice asks bob for a donation to a specific charity, and bob responds with an evasive answer such as 'well, you know....' in this situation, we can assume that bob is unwilling to donate, but since refusing the donation is psychologically burdensome, he wants alice to sense his hesitation. the speaker's intentions can be conveyed without saying them out loud, and they also vary depending on the context of the conversation. we engage in conversations while estimating the speaker's intentions unconsciously, and this ability is essential for facilitating natural communication. although llms perform well in most existing nlp tasks and are known to have high linguistic knowledge, few works focus on exploring their ability to detect speakers' intentions in conversations. this study creates a dataset to measure llms' intention detection capability in persuasive conversations. this dataset consists of multiple-choice questions that ask llms to identify the speakers' intentions in conversations. unlike prior studies focused on single-turn utterances, detecting intentions within a conversation requires considering the context of previous utterances. moreover, in persuasive conversations, making requests or replies that are inconvenient for others or even hurt others' feelings is inevitable. therefore, speakers should consider others' feelings or perspectives more carefully through their utterances than in daily conversation. these features are suitable for measuring intention detection capability in multi-turn dialogues. in the dataset creation, we employ the concept of face @xcite , a desire related to human relationships in social life. by focusing on specific utterances that influence face, we can measure the ability to detect intentions of crucial speech that affect the interlocutor's emotions. moreover, grouping similar types of intentions by applying face enhances the clarity of analysis, leading to improved insights. after creating the dataset, we verified whether llms can detect intentions from utterances. we analyzed several llms' intention detection capabilities and identified the types of intentions that are particularly challenging for them. this research makes the following two contributions. first, we constructed a dataset for measuring intention detection capability from persuasion dialogues. this dataset follows the format of comprehension problems from previous studies. second, we evaluated how well state-of-the-art llms such as gpt-4 and chatgpt detect the intention of utterances in dialogues. we provide insights into mistakes made by llms and intentions that are challenging to comprehend. ## background this section first explains face and face acts and the existing dialogue data utilized in our research. after that, we discuss previous studies on dialogue comprehension and intention detection. ## data as mentioned in the previous section, prior studies on intention detection mostly did not apply multiturn dialogue data. a possible approach to evaluate intention detection capability is utilizing the persuasive dialogue dataset created in @xcite and directly predicting face acts from utterances. however, considering that face acts are abstract intentions and are not well-known concepts, they are non-intuitive for humans to handle. also, they are likely not sufficiently acquired by llms in incontext learning, as face acts should be infrequent in the text data for pretraining. thus, modifying the task into an applicable format in zero-shot or few-shot scenarios is necessary to evaluate llms' intention detection capability instead of just employing face act prediction tasks straightforwardly. we modify persuasive dialogue data foot_2 in @xcite and create a dataset for evaluating intention detection capability. instead of directly predicting face acts, we transform face acts into intention descriptions written in natural language to make the task comprehensible. each entry in our dataset is represented in figure 1 . the input of this task consists of conversational history and four intention descriptions for the last utterance in the conversation. the output is one description out of four options. this format is a reading comprehension style inspired by several previous dialogue reasoning studies @xcite and frequently employed for evaluating llms' reasoning ability. this study aims to create evaluation data for evaluating intention detection capability. therefore, we partitioned the persuasive dialogue dataset into training, development, and test data in an 8:1:1 ratio and only utilized the test subset. in this section, we describe how we developed the evaluation dataset. first, we outline how we defined intention descriptions that will be annotated into utterances. then, we detail how we annotated descriptions for each utterance through crowdsourcing. lastly, we clarify how we selected three distractors to create four options. dutt et al. (2020) presented several intention descriptions found in persuasive situations with corresponding face acts. we adapted and expanded upon these descriptions, which were then annotated to correspond with specific utterances. specifically, we devised new descriptions to encompass all utterances in the development data and refined broader intention descriptions into more specific versions. we curated 42 descriptions listed in ## experiment we evaluate how well llms detect intentions from utterances in persuasive dialogues. we employed various sizes of llms to observe how the model size affects the intention detection capability. among llms released by openai, we employed gpt-4 and chatgpt. other smaller models are llama 2-chat @xcite from meta and vicuna @xcite from lmsys. the provided prompts to llms include information for detecting intentions of the utterance: conversational situation and task explanation, conversational script, and a four-optional question. we designed the prompt according to the zero-shot chain-of-thought style @xcite , dividing the answering process between the reason explanation and option selection phases. see appendix f for details of the prompt we created. in the er praises or promotes the good deeds of stc. ee presents their knowledge about charities to er. er states that stc is a reputable and trustworthy organization. ee insists that they are proud of themselves. er states that stc provides information on donations or other related matters, implying that stc engages in beneficial activities for society. ee claims that they have donated to charities other than stc or participated in their activities. er shows their involvement for stc, such that they are going to donate to stc or have done so in the past. ee expresses their preference for charities or the targets they want to help. er expresses their preference for charities or the targets they want to help. ee claims that they want to do something good, such as helping children. er claims that they want to do something good, such as helping children. er claims that they have donated to charities other than stc or participated in their activities. er insists that they are proud of themselves. spos-ee apologizes for not making a donation or for making only a small one. hpos+ er appreciates or praises ee's generosity. ee shows willingness to donate or to discuss the charity. er empathizes or agrees with ee. ee empathizes or agrees with er. er encourages ee to do good deeds, other than donating to stc. ee acknowledges the efforts of stc. er is interested in the organization mentioned by ee and plans to research it later. ee states that they know about stc by name, but they are not so familiar with the organization. er compliments ee for their virtues, efforts, likes or desires. ee appreciates or praises er's generosity. er motivates ee to donate to stc, such as by explaining the essential role their donation plays in helping children or highlighting the suffering children endure due to war, poverty, and other hardships. ee is planning to browse the website recommended by er. to benchmark human performance, we hire workers from amt to solve the task. they have already taken a pilot test, as we mentioned in section 3.2, and have proven to be able to provide highquality annotation. they do not join in the annotation process for the test data and we guarantee that they do not know the gold intention description for each utterance. workers read through the presented conversation and select the intention description of the last utterance from four options. the final answer is determined by a majority vote among the three workers' choices. if the three workers ## conclusion this study investigates whether llms can detect intentions in multi-turn persuasive dialogues. we utilized existing persuasive dialogue, and designed a framework for building datasets and conducting detailed analyses to evaluate llms' intention detection capabilities in conversation. although this research is confined to the narrow conversational situation, we did not employ unique methods that relied on the specific situation. therefore, the insights gained from this study are likely applicable to various dialogues, and we can conduct similar analyses in different dialogue genres. in this study, we solely created a dataset for evaluation purposes. the availability of training data for finetuning pre-trained language models is essential, and that would be our future study.