Therapist Self-Disclosure as a Natural Language Processing Task (2024)

Abstract: Therapist Self-Disclosure (TSD) within the context of psychotherapy entails the revelation of personal information by the therapist. The ongoing scholarly discourse surrounding the utility of TSD, spanning from the inception of psychotherapy to the present day, has underscored the need for greater specificity in conceptualizing TSD. This inquiry has yielded more refined classifications within the TSD domain, with a consensus emerging on the distinction between immediate and non-immediate TSD, each of which plays a distinct role in the therapeutic process. Despite this progress in the field of psychotherapy, the Natural Language Processing (NLP) domain currently lacks methodological solutions or explorations for such scenarios. This lacuna can be partly due to the difficulty of attaining publicly available clinical data. To address this gap, this paper presents an innovative NLP-based approach that formalizes TSD as an NLP task. The proposed methodology involves the creation of publicly available, expert-annotated test sets designed to simulate therapist utterances, and the employment of NLP techniques for evaluation purposes. By integrating insights from psychotherapy research with NLP methodologies, this study aims to catalyze advancements in both NLP and psychotherapy research.

## introduction therapist self-disclosure (tsd) has various definitions in the literature (e.g., @xcite @xcite @xcite ), but the one theme that unites these definitions is that tsd involves a therapist's personal self-revelatory statements. in other words, such statements are those that reveal something personal about the therapist. this definition refers to verbal disclosures and excludes disclosures that are nonverbal @xcite . the attitude toward the use of tsd in psychotherapy has changed over the years. classical psychoanalytic clinicians tended to emphasize the importance of the therapist's anonymity, equanimity, and abstinence @xcite . many of them viewed tsd as a boundary violation and believed it derailed therapy by removing the focus from the client @xcite . over the years, however, therapists and theorists across diverse orientations have increasingly converged around the perspective that tsd can yield a range of positive outcomes when employed purposefully and thoughtfully and that refraining from tsd in every instance may potentially lead to adverse consequences for both the client and the overall therapeutic process @xcite @xcite @xcite . the first to embrace a pro-disclosure approach were the humanistic theorists @xcite . they have postulated that therapists can demonstrate openness, strength, vulnerability, and the sharing of intense feelings cautiously through tsd. by doing so, they invite the client to follow suit and cultivate an environment of openness, trust, intimacy, gains in self-understanding and change @xcite @xcite . cognitive-behavioral therapists describe tsd as a tool that is useful for strengthening the therapeutic bond, normalizing clients' experiences of their difficulties, challenging negative interpretations of emotions and behavior, enhancing positive expectations and motivation for change, and modeling and reinforcing therapist self disclosure (tsd) therapist reveals something personal about himself ## related work in this section, we review the existing works (both from clinical psychology and nlp literature) that refer to the evaluation of self-disclosure. in our review, we refer to the domain of the source (psychotherapy, nlp, or hybrid); the method used to determine self-disclosure (self-report questionnaire, crowdsourcing, experts, analysis or randomized clinical trail); the resolution of the data that was investigated (utterance, event, session or treatment); the type of the data (clinical, non-clinical, or pseudo clinical); whether a public test set has been published; the speaker identity (therapist, listener, or chatbot); and whether referring to the subcategories (immediate and not immediate or only self-disclosure in general). in the next sections, we provide an extensive literature review of both psychotherapy (section 2.1) and nlp (section 2.2) approaches for this task. ## data in this section, we describe the creation of two test sets: expert-tsd and mi'. the first was developed from scratch by an expert, and the second was created by expertly annotating an existing dataset. both test sets are in english. 2 the purpose of the first test set is to provide an adequate test for tsd (precision). the purpose of the second test set is to strengthen the findings and to enable an assessment of real data distribution. real data contains surprising behaviors such as syntax and grammar errors, informal or non-verbal utterances, and more phenomena. it is important to examine behavior in a wide variety of situations (recall) to strengthen our conclusions. the subsequent paragraphs provide the construction process for each test set. expert-tsd. the initial phase of the test set creation process involved a collaborative effort between the authors (an nlp researcher and a clinical psychologist specializing in tsd research). in a comprehensive brainstorming session, the authors discussed the precise definition of tsd and its subtypes as described in psychotherapy literature (see section 1 and next, utterances were generated by the clinical psychologist along with their respective type label. the nlp researcher reviewed the proposed samples marking potential shallow heuristics, such as syntactic features, that a machine learning model might exploit to predict the correct label for the incorrect reasons (see shallow heuristics @xcite @xcite @xcite . this writing and reviewing procedure was conducted throughout five iterations, with new samples proposed and previous ones fixed. to mitigate the effect of shallow heuristics, we made sure to diversify utterances over the following properties: (1) the balance of positive and negative examples (i.e., including "not a tsd" utterances) (2) the length of the utterance (i.e., short sentence below 10 words vs. numerous or long sentences above 20 words), (3) the presence or absence of first-person pronouns words (e.g., i, me, our), (4) the existence of positive or negative sentiment, and (5) the incorporation of questions. the test set generation rounds were stopped once we surpassed 100 instances (108), which is a sufficient quantity for testing significance. mi'. we first sampled 650 examples from the mi dataset @xcite , summarized in section 2.2), ensuring diversity by extracting 25 instances from each category and 300 from selfdisclosure. for each utterance, the tsd expert annotated the tsd type based on the task definition outlined in section 1 and a total of 277 items were tagged. an effort was made to equally represent each class ("immediate tsd", "non-immediate tsd" and "not a tsd"). except for one instance, all of our utterance labels agreed with the mi labels for the binary self-disclosure classification. therapist self-disclosure instructions prompt test: below are definitions of two subcategories of self-disclosure and not self-disclosure: non-immediate tsd: self-disclosure of information about the therapist. * relates to disclosing, during a treatment session, facts about the therapists' life outside of the treatment and personal insights they gained, the way they reached these insights, effective / in-effective ways of coping based on their experience and the way they formulated them, emotions that they experience in different situations in their life, etc... * example: speech turn: i remember going through a career change a few years ago, and it was a challenging time for me. it's normal to feel uncertain during transitions, but it's also a chance to explore new possibilities. answer: non-immediate tsd immediate tsd: self-disclosure that relates to the "here and now". * relates to sharing therapists' feelings, associations, and thoughts relating to the client and the issues and topics raised during the session and of their emotions, feelings, and thoughts on the therapy process which they are both part of, etc. ## method in line with the latest works that examine automated detection of psychology-related tasks by llms in-context learning or zero-shot setup (e.g., @xcite , we investigate the tsd automatic detection abilities of llms. we evaluated the two test sets mentioned in section 3 in-context learning setup. llms and decoding parameters. we used two different llms: flan-t5 (chung et al., 2022) 3 of different sizes flan-t5-{small, base, large, xl} and gpt-4 @xcite @xcite . 4 a single sample (the first) was selected from each model for the analysis of the tagging evaluation. we chose hyperparameters that minimize randomness, predict the most probable answer (i.e., low temperature, sampling method), and allow for a sufficient number of tokens. expert-tsd results. the results of the expert-tsd test set appear in as evident, flan-t5 exhibits a bias toward the "non-immediate" class. the results of gpt-4 were 3 python packages: transformers (automod-elforseq2seqlm, autotokenizer) and torch; generation function: generate; hyper-parameters: @xmath0, @xmath1, from_pretrained:{google/flan-t5-small, google/flan-t5-base, google/flan-t5-large, google/flan-t5-xl}, @xmath2.0001 4 python package: openai; generation function: chat-completion.create; hyper-parameters: @xmath3; above expected human annotation agreement; and higher than previous self-disclosure literature as reported by @xcite . note that this method is proposed for practice and as a proof-ofconcept and not for real use, see more discussions in the limitation section and ethical statement. for the gpt-4, 10% utterances where discrepancies emerged between the labels assigned by the human annotator and those generated by gpt-4, we conducted a manual error analysis and consulted with three additional psychotherapists. notably, there was no consensus among the therapists regarding whether these utterances constituted tsd. upon examining the inconsistencies in labeling between the human annotator and gpt-4, it became apparent that the discrepancies pertained solely to immediate tsd. specifically, two types of differences were identified: first, instances where the human annotator identified "immediate tsd" while gpt-4 identified "not a tsd"; and second, cases where gpt-4 detected "immediate tsd", but the human annotator detected "not a tsd". determining the frequency of immediate tsd in real therapy sessions poses a considerable challenge. therapists and clients typically perceive these interventions as integral to the therapeutic dialogue, leading to their routine exclusion from tsd reports. nevertheless, it is assumed that such disclosures transpire more frequently in therapeutic dialogues than what has been officially reported @xcite . moreover, as for instances where gpt-4 identified immediate tsd, but the human annotator did not, it appears that some of the utterances were characterized as immediacy. the term immediacy was defined by @xcite as "discussion of the therapeutic relationship by both the therapist and client in the here-andnow, involving more than social chitchat". while earlier literature used immediacy to refer to immediate tsd utterances, researchers have evolved from defining immediacy exclusively as immediate tsd and now use the term to refer to a more complex phenomenon @xcite . immediacy extends to therapist responses and behaviors such as feedback, inquiries to gather more information about the client's here-and-now reactions, and primary and advanced empathy to reflect the client's momentary experiences. at times, immediacy utterances are more client-focused, than therapist-focused. an illustrative example from our data involves the utterance: "i've noticed you seem unhappy when we talk about the disagreement we had last time. do you think there might be some anger or resentment towards me?" the human annotator labeled it as "not a tsd," while gpt-4 tagged it as "immediate tsd," when in fact it represents immediacy. this clarification aims to shed light on some of the observed gaps in labeling. ## conclusion in this study, we have formalized therapist self-disclosure (tsd) as a natural language processing (nlp) task by introducing expert-annotated test sets to simulate therapist utterances and utilizing large language models (llms) for in-context learning as a solution. this work demonstrates how psychotherapy literature can help capture language nuances. in addition, this work shows the potential of nlp tools to enhance theoretical understanding of existing issues in psychotherapy. the contribution to the nlp domain lies in the task's potential to serve as a challenging benchmark for optimizing results of accuracy or efficiency while the proposed method serves as a baseline. in addition, the expert-annotated utterance set can function as a test set for model evaluation (as in this study) or as valuable training examples for few-shot learning or other methods. in the field of psychotherapy research, our study offers carefully documented guidelines and a testing ground for human annotators aiming to engage in manual annotations of tsd. our proposed method lays a promising foundation, however, it necessitates ongoing exploration and refinement before implementation. future research will have to examine its readiness and effectiveness for automated tsd tagging in real-world data contexts.