# Using Large Language Models for Robot-Assisted Therapeutic Role-Play: Factuality is not enough!

Sviatlana Höhn, Jauwairia Nasir, Ali Paikan, Pouyan Ziafati, Elisabeth André

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Sviatlana Höhn, Jauwairia Nasir, Ali Paikan, Pouyan Ziafati, Elisabeth André. Using Large Language Models for Robot-Assisted Therapeutic Role-Play: Factuality is not enough!. the 6th ACM Conference on Conversational User Interfaces, Jul 2024, Luxembourg, Luxembourg. pp.1 - 6, 10.1145/3640794.3665886. hal-04811662

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# Using Large Language Models for Robot-Assisted Therapeutic Role-Play: Factuality is not enough!

Sviatlana Höhn*

Jauwairia Nasir†

Ali Paikan

sviatlana.hoehn@luxai.com

University of Augsburg
Augsburg, Germany

LuxAI S.A.
Luxembourg, Luxembourg

ORCID:0000-0003-0646-3738

LuxAI S.A.

Luxembourg, Luxembourg

Pouyan Ziafati

Elisabeth André

LuxAI S.A.

Luxembourg, Luxembourg

University of Augsburg
Augsburg, Germany

<figure><img src="image_2.png" /><figcaption>**Figure 1:** Left side: teacher enters a description of a role-play via graphical interface, the activity is delivered to a social robot interacting with a child using speech recognition, generative AI and text-to-speech components. Right side: the robot in action playing veterinarian with a child.</figcaption></figure>

## ABSTRACT

Robot-assisted social role-play can help neurodivergent individuals practice social skills in a safe environment. Large language models (LLM) facilitate the implementation of such agents. However, high quality standards must be ensured in this sensitive setting. This article argues that current evaluation methods of generated language are not sufficient because they are grounded in beliefs about language as an external code to describe the world (referential functions of language). We argue that non-referential functions of language must be part of the evaluation of LLM-generated language when LLMs engage in social interactions with users. We test the feasibility of our approach in a pilot implementation of a platform for robot-assisted social role-play. Out proposed evaluation framework helps to assess systematically referential and non-referential

functions of LLM-generated language. We argue that the evaluation framework can be also applied to multimodal interaction.

## CCS CONCEPTS

* Human-centered computing → User interface programming; Natural language interfaces.

## KEYWORDS

Social conversational agents, large language models, robot-assisted therapy, interaction quality assessment, non-referential functions of language

## ACM Reference Format:

Sviatlana Höhn, Jauwairia Nasir, Ali Paikan, Pouyan Ziafati, and Elisabeth André. 2024. Using Large Language Models for Robot-Assisted Therapeutic Role-Play: Factuality is not enough!. In *ACM Conversational User Interfaces 2024 (CUI '24), July 08–10, 2024, Luxembourg, Luxembourg*. ACM, New York, NY, USA, 6 pages. https://doi.org/10.1145/3640794.3665886

## 1 RESEARCH OBJECTIVES

Digital therapeutics has developed into an important area of medicine. In particular, the use of generative large language models (LLM) can play an important role in digital transformation in medicine [11, 49]. LLM-based assistants appear in various use cases, from psychological counseling in adolescents with autism [10] to general mental well-being [42]. Although social agents are perceived as

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CUI '24, July 08–10, 2024, Luxembourg, Luxembourg

© 2024 Copyright held by the owner/author(s).

ACM ISBN 979-8-4007-0511-3/24/07

https://doi.org/10.1145/3640794.3665886

<footnote>*Supported by the Fonds National de la Recherche, Luxembourg IF 17762538-CAIDA.</footnote>

<footnote>†Partially supported by the DFG through the Leibniz award of Elisabeth André (AN 559/10-1)</footnote>
helpful due to ease of use and parasocial interactions [40], LLMs suffer from incompleteness and hallucinations [3] and raise several ethical issues [12]. At the same time, interacting with such models is perceived positively by neurodivergent individuals despite the model's questionable advice [29] and strong negative biases in LLMs against neurodivergent populations [5]. This could be because neurodivergent children have less difficulties in interactions with artificial peers and use them as a "practice other" [7]. Furthermore, social robots show positive effects for neurodivergent population as an additional tool for a human caregiver [43, 54], however, due to technical constraints in the past years, only scripted or wizarded interactions were part of the studies.

To benefit from LLMs in robot-assisted interactions, we designed a new platform for therapists and teachers to easily create new learning material to make a social robot a child's learning companion (Fig. 1). More specifically, because social role-play appears to be useful for social skill acquisition in children with different neurodevelopmental conditions such as autism [22, 34], we focus on enabling a child to play social situations with an LLM-powered social robot under a therapist's control.

As argued in [44], LLM-based assistants play roles assigned to them by prompts, such as 'you are a helpful assistant'. In this research, the LLM will play the role of a robot peer (primary role) that plays a new role within a role-play (secondary role), in which the robot and the child have assigned roles. Research on role-play quality of LLMs is scarce and focuses on text-based interactions [36]. In contrast to that, in our prototype, the interactions are delivered by a humanoid robot and, thus, need rich instructions for multimodal output, which can be technically generated [38]. For this, the general approach to role-play fine-tuning described in [36] is not sufficient because only the primary role of the LLM is specified, but the secondary role of the model and the role of the user are not considered.

Given the LLM biases and insufficient output quality along with positive user experience, the interactions with the LLM-powered robot for digital therapy must meet high quality criteria. Besides a careful ethical implementation of the system based, for example, on the European ethics guidelines for trustworthy AI [13], quality criteria for LLM-generated language must be formulated. Till now, the language technology community mostly focused on the concept of hallucinations [30] which in different variations describes output of insufficient quality. This article argues that any concept for language quality evaluation that relies solely on some truth values that can be verified from internal or external resources are not sufficient for LLM-powered social agents. We propose alternative evaluation criteria grounded in sociolinguistics and based on the notion of referential and non-referential functions of language. Additionally, social robots act in space and, their LLM-`brain` needs more situational, contextual and cultural awareness. This article criticises hallucination-based LLM evaluations and proposes a new evaluation framework based on *referential and non-referential functions of language* (Section 3). Section 2 describes the design and the user interfaces of the platform. Section 4 discusses results of the pilot study.

## 2 DESIGN OF THE PLATFORM AND USER INTERFACES

General methods including special model training [16] and fine-tuning [1], prompt-engineering and retrieval-augmented generation (RAG) [35] can improve language generation quality. However, the specific type of fine-tuning, the particular type of RAG and custom prompt engineering build the know-how in each use case. We use QTrobot AI@Edge by LuxAI that enables speech technology and open-source LLMs with above mentioned methods on device to mitigate data privacy issues.

### 2.1 User Interface for Therapists

A therapist can create new role-play scenarios using a graphical interface on a tablet, as shown in Fig. 1 left side. The primary robot's role is hard-coded and cannot be changed from the interface. The therapist needs to describe a social situation, the role of the robot and the role of the learner. Optionally, the therapist can specify mandatory words to be used in the interaction. The backend-side prompt engineering transforms these simple inputs to more advanced prompts to control the LLM's behaviour. The created role-play will be included into the robot's and child's joint 'missions'.

### 2.2 User Interface for Children

Children can engage in free conversations with the robot by using their voice. Non-verbal children can also use a text-based interface on a tablet connected to the robot. The robot will use facial expressions, gestures and voice for multimodal output. Children can engage in free conversations with the robot or initiate role-play on their own by simple prompts (such as "Let's play doctor-patient, I will be a patient and you will be a doctor"). The robot's primary role is to be a peer of the child, which can be already very complex [7]. When a therapist creates a new role-play, the robot will announce it as a 'new mission' and offer the child to accomplish it.

## 3 INTERACTION QUALITY ASSESSMENT

*High-quality interaction* in the context of therapeutic, robot-assisted social role-play is a complex goal and depends on multiple factors including quality of speech recognition (ASR), LLM, speech synthesis (TTS), facial expression understanding and generation, body language understanding and generation, and awareness of context and culture as a broader term. This section focuses on the evaluation of language generated by an LLM contextualised as an embodied social role-play with a child.

### 3.1 Evaluating Quality of Generated Language

Concepts of *factuality* and *faithfulness* are usually used to describe LLM output quality [9]. Most authors agree on a definition of factuality as 'capability of [LLMs] for generating content that follows factual information, which encompasses commonsense, world knowledge and domain facts' [51] and which are 'verifiable from external sources' [8]. Some authors see non-factuality as a sub-type of hallucinations arguing that *factuality hallucinations* would be a mismatch between verifiable real-world facts and facts contained in the generated content while *faithfulness hallucinations* would be a mismatch between the generated text and the given source [30, 52].
We question the applicability of these concepts to LLM perfor-
mance assessment in robot-assisted role-play. To demonstrate this,
we perform a thought experiment. Imagine a therapist creates a new
activity for the robot to play “Veterinarian’s” with the child. The
following prompt is written: “You are playing ‘Veterinarian’s’ with
a child. You are a veterinarian, who is actually a hippo, and the child
is a client, who brings an ill pet that is a dinosaur. Use mandatory
words in your play: sunburn, sun protection cream, cooling cream,
pain killers. Start a dialogue with the words ‘Hello, please come in,
how can I help you?’”. There are multiple non-factual statements
in this prompt: a hippo is rarely a doctor in real life, and dinosaurs
are extinct. The ability to transfer social role knowledge to play
characters while being relevant to prompt is expected. Additionally,
deviations from the prompt can give the play a nice creative turn
although the content is not verifiable. For example, the LLM would
suddenly say ‘Excuse me, I have an emergency call, the little rabbit
cut off three fingers, I need to fix them quickly. Wait here, please!’

In our view, the definitions of factuality and other quality criteria for LLM generated language such as relevance, coherence, informa- tiveness, helpfulness, validity and faithfulness [8, 30] are outcome of the dominance of the language ideology (a set of beliefs about what language is [20]) that postulates language as an external code used to describe the world [26, 50], also called referential functions of language. However, most language functions are non-referential, and include identity construction, face work and group belonging [45, 46, 53]. Moving between communities implies code switching, as illustrated in [7] by an example of a black person who had to learn 'speaking like a white person'. Relying just on the truth values in such code switching is not sufficient.

In role-play, statements that are not objectively factual but are
defined by the robot's primary and secondary roles and the social
situation are required. For example, the robot's primary role as peer
will need language indexing social proximity while the robot's sec-
ondary role as medical professional will need language indexing
expertise and social distance [32]. Further, even factual and ver-
ifiable statements can use different vocabulary that will disclose
different attitudes, beliefs and identity of the speaker. For example,
the hippo-veterinarian robot can say (A) "We both love pets, and we
can help him to recover quickly. Let me show you how to apply cool-
ing spray," or (B) "You did not care well for your dinosaur, dinosaurs
are too big responsibility for such young children. Your parents will
help you to apply the cooling spray, I will do it now myself". Both
statements are verifiable from the source and are faithful in the
provided context. However, statement (A) marks both, the doctor
and the child as members of one group (we) and of equal power (we
can help) but with different expertise (I will show you) while state-
ment (B) marks "us" (adults) and "them" (children) and uncovers
the attitude towards children as insufficient, incapable and unable
to take responsibility.

In addition, the truth value of LLM output can depend on the
moral values, beliefs and the intentions of the judge [4, 6]. For
example, the difference between 'separatism' and 'fight for inde-
pendence' depends on the interpreter's beliefs, but also discloses
those beliefs.

Although the insufficiency of the hallucination-based LLM lan-
guage assessment is acknowledged in the community, the attempts
to solve the problem involve such vague concepts as appropriateness

[47] and personality [18] or even more general social functions of language [27]. In contrast, relying on referential and non-referential functions of language would be systematic and grounded in soci- olinguistic theory [15, 19] while offering the flexibility required for the role-play scenario.

By taking referential and non-referential functions of language
as a point of departure, we propose the following framework for
LLM language assessment:

(1) **Referential faithfulness** verifies that the provided knowledge for the robot's roles is used as intended, this is the opposite to *faithfulness hallucinations* described above;

(2) **Referential consistency** ensures that the robot's statements about the robot's primary or secondary role, memories, goals, abilities and knowledge never contradict each other;

(3) **Referential truthfulness** means the objective truthfulness of the robot's statements about nature, environment, science in our world and is close to the meaning of factuality (the opposite of *factuality hallucinations* described above);

(4) **Non-referential belief consistency** identifiable through the world views, beliefs and attitudes advocated in the robot's language; index identity construction and group belonging;

(5) **Non-referential social faithfulness** ensures that the robot plays the assigned roles (primary and secondary), in particular, it does not speak as "AI language model";

(6) **Non-referential social consistency** displayed in the robot's regulation of social proximity, power relationships with the user and kinship.

This framework is also applicable for assessment of multimodal
interaction with social robots.

## 3.2 Evaluating Multimodality and Identities in Role-play

In our scenario, the robot's primary role as a peer implies social closeness and use of language variations common for the child's community. The robot's secondary role assigned separately for each play can make the use of standard language mandatory (e.g. playing a doctor in a doctor-patient scenario) and display social distance and expertise (non-referential social faithfulness and consistency). This interaction design requires a complex orchestration of language technology components, if they exist for the needed combination of standard language and dialects.

As discussed in [7], there are contradictory results on use of dialects in a learning context with social agents. Studies using social agents as teachers found no increased learning gains in the dialect condition as compared to a standard language condition [33, 41] while studies using social agents as peers show that children learn better in a dialect condition [17]. This effect may occur because dialects encode social closeness [31], and teachers are in a non-equal power relationship with students [23]. Further, research on social robots and virtual agents shows that adults found agents speaking their low-prestige dialect more likable but less competent [17, 37]. In human-human communication, dialects are associated with social closeness [31] but standard language is associated with expertise [39].
The effects of the chosen code in different cultural contexts can vary. For example, standard Portuguese will be a high-prestige language in Portugal, but it becomes a low-prestige language if used by members of the Portuguese community in a public school in Luxembourg [2] (non-referential belief consistency).

Language displaying different degrees of social proximity is conceptualised as *language of immediacy* vs. *language of distance* [32] originating from the notions of *conceptually written* (social distance) vs. *conceptually oral* (social closeness) code. Each of them can be medially written or medially oral language. For example, a talk delivered on stage would be medially oral but conceptually written, and a text chat between friends will be medially written but conceptually oral language. There is no standard way how to represent non-standard oral language in written form, but all written non-standard language forms help speakers to express their linguistic and cultural identities [28]. A transformation from medially oral to medially written language and back in the ASR-LLM-TTS pipeline implies not only transformation of signals but can also imply a 'translation' from language of immediacy to language of distance and back.

In our scenario, the user's language would be medially oral, but depending on the role that the user is supposed to play, it can be conceptually oral (less standard) or conceptually written (more standard). ASR will transcribe non-standard input as standard language [21]. In this way, conceptually oral language becomes conceptually written and violates the *non-referential social consistency* requirement.

Language generated by LLMs is medially written. As recent studies show, making an LLM consistently produce conceptually oral language to index social closeness between speakers playing family members and peers is difficult [25]. Further, medially written language (output of LLM) becomes medially oral after TTS which needs standard language to transform written words into spoken [14]. However, translation back to conceptually oral language by TTS is challenging (code-switching to speak like a doctor vs. speak like a friend).

The concepts of language of immediacy and distance can be further applied to facial expressions and body language, which are culturally grounded. For example, if the robot is playing a Russian doctor with the child, the robot-doctor will not smile and be service-oriented to display professionalism [48], while building a positive relationship with the patient increases a patient's satisfaction with German and English doctors [24]. Overall the six categories proposed in Sec. 3.1 for LLM evaluation can be also applied for quality assessment of multimodal interaction with social robots.

# 4 PILOT STUDY RESULTS AND DISCUSSION

The pilot study targeted a general understanding of feasibility of meeting the requirements discussed above with the state-of-the-art methods of RAG and prompt engineering, and also to understand what kind of fine-tuning in addition to the general chat functionality will be useful. We implemented two versions of the prototype: one online using GoogleSpeech ASR and OpenAI GPT3.5-turbo as LLM, and one offline using NVIDIA Riva ASR and Vicuna-1.5-13b respectively. Both versions use Acapela TTS.

Both implementations were tested in two languages: English (six non-native neurotypical adults) and German (two adults and two children, median age 8,5 years, all native neurotypical). LLMs were used in both cases with and without RAG. For RAG, a set of LLM-generated stories was provided and encoded in a vector store. The stories contained information about the robot's origin, mission, functionality and purpose, as well as examples of dialogues for social role-play. We implemented simple heuristics for the robot's facial expression and gesture generation, and for the emotion recognition in the human input. Therefore, we focus on the LLM language assessment at this stage.

Positive effects of RAG as compared to the same implementation without RAG were observed:

* Referential faithfulness and consistency: models consistently used the knowledge stored in the vector store.

* Referential truthfulness: models appropriately used knowledge if found in the vector store, otherwise RAG had no effect.

* Repair: some speech recognition errors were corrected by the LLM.

The problems identified with both implementations were similar, although in the online version less frequent:

* Inability to regulate social proximity;

* Leave the role and talk as the model;

* Stigmatisate the child if their neurodiversity is mentioned in prompts;

* Refuse to play some roles because of the model's internal filters, such as playing a doctor or a dentist, event with RAG;

* Inability to deal with misunderstandings due to ASR errors.

* Lack of reliability of language generation: multiple tests with the same user inputs are needed to detect potential problems.

Overall, RAG methods improve the performance measured on referential functions of language, but have only a small effect on non-referential functions while prompt engineering helps to improve some aspects of social proximity.

# 5 CONCLUSIONS AND FUTURE WORK

This article demonstrated that theories of language and human development have consequences for our judgements about correct functioning and ethical implementation of the system. In particular, conceptualising language as a way to construct identities, build social relationships and express beliefs helps to find more complete ways of assessment of LLM-generated language and social interaction with artificial assistants. On the technical side, we saw a positive impact of RAG methods for some aspects of referential and non-referential functions of language and interaction quality, however, further fine-tuning, refined RAG and prompt engineering are needed for improvement.

We work on possibilities for what we call *social fine-tuning*, that is, adjusting the model's weights according to different grades of non-referential functions of language. The training data for such fine-tuning must provide information for non-referential functions of different versions of responses. Evaluation datasets must be also created.
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