Guillermo A. Cecchi

Also published as: Guillermo Cecchi


2024

pdf bib
Towards Healthy AI: Large Language Models Need Therapists Too
Baihan Lin | Djallel Bouneffouf | Guillermo Cecchi | Kush Varshney
Proceedings of the 4th Workshop on Trustworthy Natural Language Processing (TrustNLP 2024)

Recent advances in large language models (LLMs) have led to the development of powerful chatbots capable of engaging in fluent human-like conversations. However, these chatbots may be harmful, exhibiting manipulation, gaslighting, narcissism, and other toxicity. To work toward safer and more well-adjusted models, we propose a framework that uses psychotherapy to identify and mitigate harmful chatbot behaviors. The framework involves four different artificial intelligence (AI) agents: the Chatbot whose behavior is to be adjusted, a User, a Therapist, and a Critic that can be paired with reinforcement learning-based LLM tuning. We illustrate the framework with a working example of a social conversation involving four instances of ChatGPT, showing that the framework may mitigate the toxicity in conversations between LLM-driven chatbots and people. Although there are still several challenges and directions to be addressed in the future, the proposed framework is a promising approach to improving the alignment between LLMs and human values.

pdf bib
Working Alliance Transformer for Psychotherapy Dialogue Classification
Baihan Lin | Guillermo Cecchi | Djallel Bouneffouf
Proceedings of the 6th Clinical Natural Language Processing Workshop

As a predictive measure of the treatment outcome in psychotherapy, the working alliance measures the agreement of the patient and the therapist in terms of their bond, task and goal. Long been a clinical quantity estimated by the patients’ and therapists’ self-evaluative reports, we believe that the working alliance can be better characterized using natural language processing technique directly in the dialogue transcribed in each therapy session. In this work, we propose the Working Alliance Transformer (WAT), a Transformer-based classification model that has a psychological state encoder which infers the working alliance scores by projecting the embedding of the dialogues turns onto the embedding space of the clinical inventory for working alliance. We evaluate our method in a real-world dataset with over 950 therapy sessions with anxiety, depression, schizophrenia and suicidal patients and demonstrate an empirical advantage of using information about therapeutic states in the sequence classification task of psychotherapy dialogues.

2019

pdf bib
Nearly-Unsupervised Hashcode Representations for Biomedical Relation Extraction
Sahil Garg | Aram Galstyan | Greg Ver Steeg | Guillermo Cecchi
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Recently, kernelized locality sensitive hashcodes have been successfully employed as representations of natural language text, especially showing high relevance to biomedical relation extraction tasks. In this paper, we propose to optimize the hashcode representations in a nearly unsupervised manner, in which we only use data points, but not their class labels, for learning. The optimized hashcode representations are then fed to a supervised classifi er following the prior work. This nearly unsupervised approach allows fine-grained optimization of each hash function, which is particularly suitable for building hashcode representations generalizing from a training set to a test set. We empirically evaluate the proposed approach for biomedical relation extraction tasks, obtaining significant accuracy improvements w.r.t. state-of-the-art supervised and semi-supervised approaches.

2017

pdf bib
Using Automated Metaphor Identification to Aid in Detection and Prediction of First-Episode Schizophrenia
E. Darío Gutiérrez | Guillermo Cecchi | Cheryl Corcoran | Philip Corlett
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

The diagnosis of serious mental health conditions such as schizophrenia is based on the judgment of clinicians whose training takes several years, and cannot be easily formalized into objective measures. However, previous research suggests there are disturbances in aspects of the language use of patients with schizophrenia. Using metaphor-identification and sentiment-analysis algorithms to automatically generate features, we create a classifier, that, with high accuracy, can predict which patients will develop (or currently suffer from) schizophrenia. To our knowledge, this study is the first to demonstrate the utility of automated metaphor identification algorithms for detection or prediction of disease.

2010

pdf bib
The emergence of the modern concept of introspection: a quantitative linguistic analysis
Iván Raskovsky | Diego Fernández Slezak | Carlos Diuk | Guillermo A. Cecchi
Proceedings of the NAACL HLT 2010 Young Investigators Workshop on Computational Approaches to Languages of the Americas