Mexico

Electronic health records (EHR) and claims data are rich sources of real-world data that reflect patient health status and healthcare utilization. 
Querying these databases to answer epidemiological questions is challenging due to the intricacy of medical terminology and the need for complex SQL queries. Here, we introduce an end-to-end methodology that combines text-to-SQL generation with retrieval augmented generation (RAG) to answer epidemiological questions using EHR and claims data. We show that our approach, which integrates a medical coding step into the text-to-SQL process, significantly improves the performance over simple prompting. Our findings indicate that although current language models are not yet sufficiently accurate for unsupervised use, RAG offers a promising direction for improving their capabilities, as shown in a realistic industry setting.

NAACL 2024

Retrieval augmented text-to-SQL generation for epidemiological question answering using electronic health records

text-to-sql; retrieval augmented generation; large language models; text generation; electronic health records

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
This year, the conference is in Mexico City. NAACL was actually already planned for Mexico City in 2021, but due to the pandemic the entire conference was moved online. This year, finally, we get to go! So it is my sincere pleasure to welcome you to Mexico City, whether in person or virtually. Having the conference in Mexico City is a good opportunity to emphasize that NAACL is our flagship conference for ACL members not only in North America but also in Central and South America, even though NAACL has been bearing “North” in its name. At this year’s conference, we have a theme to match, with a theme track on the Languages of Latin America to showcase the linguistic diversity of the region.
 
The opportunity to present at NAACL should not depend on a researcher’s travel budget, or their family status. This is why it is so important to make virtual participation at NAACL as good an experience as possible – but we want to also provide a good experience for in-person participants. As a community, we are still working out the best way to do that. This year at NAACL, we are trying out a big virtual poster session ahead of the conference, with the hope that this will make make for a lively and interactive experience. At the same time, we are reducing virtual oral presentations, which seem to be particularly tricky to make to work well. A big thanks to the NAACL program chairs and to Luciana Benotti for all their ideas and work to improve the virtual experience. And participants, virtual as well as in-person: Please let us know what worked for you and what didn’t, so we can continue to improve hybrid conferences. 
 
I have been lucky to work with many amazing people. Without their insight, dedication and patience, and without the many hours of work they put in, NAACL would not have been possible. A huge thank you to the program chairs Helena Gomez, Kevin Duh, and Steve Bethard – you are the best! 
 
Finally, I would like to thank all authors, invited speakers and panelists, area chairs and reviewers, the volunteers organizing and chairing sessions, and all attendees, in-person and virtual. Thank you for helping us make NAACL 2024 come to life. 
 
Welcome and hope you all enjoy the conference! 
Katrin Erk 
The University of Texas at Austin 
NAACL-2024 General Chair 
*You can read the full Welcome message in the [Conference Handbook (downloadable)](https://drive.google.com/file/d/1H1NvW0VASQjkSCYgw3mr-3l4yYDSxz6B/view?usp=sharing)*

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2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

workshop paper

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.

Working Alliance Transformer for Psychotherapy Dialogue Classification

Clinical Named Entity Recognition (NER) is essential for extracting important medical insights from clinical narratives. Given the challenges in obtaining expert training datasets for real-world clinical applications related to data protection regulations and the lack of standardised entity types, this work represents a collaborative initiative aimed at building a German clinical NER system with a focus on addressing these obstacles effectively. In response to the challenge of training data scarcity, we propose a Conditional Relevance Learning (CRL) approach in low-resource transfer learning scenarios. CRL effectively leverages a pre-trained language model and domain-specific open resources, enabling the acquisition of a robust base model tailored for clinical NER tasks, particularly in the face of changing label sets. This flexibility empowers the implementation of a Multilayered Semantic Annotation (MSA) schema in our NER system, capable of organizing a diverse array of entity types, thus significantly boosting the NER system's adaptability and utility across various clinical domains. In the case study, we demonstrate how our NER system can be applied to overcome resource constraints and comply with data privacy regulations. Lacking prior training on in-domain data, feedback from expert users in respective domains is essential in identifying areas for system refinement. Future work will focus on the integration of expert feedback to improve system performance in specific clinical contexts.

Building A German Clinical Named Entity Recognition System without In-domain Training Data

Biomedical NLP models play a big role in the automatic extraction of information from biomedical documents, such as COVID research papers. Three landmark models have led the way in this area: BioBERT, MSR BiomedBERT, and BioLinkBERT. However, their shallow evaluation –a single mean score– forbid us to better understand how the contributions proposed in each model advance the Biomedical NLP field. We show through a Multilevel Analysis how we can assess these contributions. Our analyses across 5000 fine-tuned models show that, actually, BiomedBERT’s true effect is bigger than BioLinkBERT’s effect, and the success of BioLinkBERT does not seem to be due to its contribution –the Link function– but due to an unknown factor.

A Multilevel Analysis of PubMed-only BERT-based Biomedical Models

Annotated corpora are essential to reliable natural language processing. While they are expensive to create, they are essential for building and evaluating systems. This study introduces a new corpus of 2,869 medical and admission reports collected by an occupational insurance and health provider. The corpus has been carefully annotated for personally identifiable information (PII) and is shared, masking this information. Two annotators adhered to annotation guidelines during the annotation process, and a referee later resolved annotation conflicts in a consolidation process to build a gold standard subcorpus. The inter-annotator agreement values, measured in F1, range between 0.86 and 0.93 depending on the selected subcorpus. The value of the corpus is demonstrated by evaluating its use for NER of PII and a classification task. The evaluations find that fine-tuned models and GPT-3.5 reach F1 of 0.911 and 0.720 in NER of PII, respectively. In the case of the insurance coverage classification task, using the original or de-identified corpus results in similar performance. The annotated data are released in de-identified form.

A Privacy-Preserving Corpus for Occupational Health in Spanish: Evaluation for NER and Classification Tasks

Large language models (LLMs) have emerged as valuable tools for many natural language understanding tasks. In safety-critical applications such as healthcare, the utility of these models is governed by their ability to generate factually accurate and complete outputs. In this work, we present dialog-enabled resolving agents (DERA). DERA is a paradigm made possible by the increased conversational abilities of LLMs. It provides a simple, interpretable forum for models to communicate feedback and iteratively improve output. We frame our dialog as a discussion between two agent types -- a Researcher, who processes information and identifies crucial problem components, and a Decider, who has the autonomy to integrate the Researcher's information and makes judgments on the final output.

We test DERA against three clinically-focused tasks, with GPT-4 serving as our LLM. DERA shows significant improvement over the base GPT-4 performance in both human expert preference evaluations and quantitative metrics for medical conversation summarization and care plan generation. In a new finding, we also show that GPT-4's performance (70%) on an open-ended version of the MedQA question-answering (QA) dataset (Jin 2021; USMLE) is well above the passing level (60%), with DERA showing similar performance. We will release the open-ended MedQA dataset.

DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents

Text generation opens up new prospects for overcoming the lack of open corpora in fields such as healthcare, where data sharing is bound by confidentiality. In this study, we compare the performance of encoder-decoder and decoder-only language models for the controlled generation of clinical cases in French. To do so, we fine-tuned several pre-trained models on French clinical cases for each architecture and generate clinical cases conditioned by patient demographic information (gender and age) and clinical features.
Our results suggest that encoder-decoder models are easier to control than decoder-only models, but more costly to train.

Using Structured Health Information for Controlled Generation of Clinical Cases in French

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.

Towards Healthy AI: Large Language Models Need Therapists Too

Language Models (LMs) have shown promising performance in natural language generation.
However, as LMs often generate incorrect or hallucinated responses, it is crucial to correctly quantify their uncertainty in responding to given inputs. In addition to verbalized confidence elicited via prompting, 
many uncertainty measures (e.g., semantic entropy and affinity-graph-based measures) have been proposed. However, these measures can differ greatly, and it is unclear how to compare them, partly because they take values over different ranges. In this work, we address this issue by developing a novel and practical framework, termed Rank-Calibration, to assess uncertainty and confidence measures for LMs. Our key tenet is that higher uncertainty (or lower confidence) should imply lower generation quality, on average. Rank-calibration quantifies deviations from this ideal relationship in a principled manner, without requiring ad hoc binary thresholding of the correctness score (e.g., ROUGE or METEOR). The broad applicability and the granular interpretability of our methods are demonstrated empirically. The code to reproduce our experiments is at https://github.com/shuoli90/Rank-Calibration/tree/main.

Uncertainty Assessment of Language Models through Rank-Calibration

This paper proposes a novel black-box approach for fact-level hallucination detection and classification by transforming the problem into a knowledge graph alignment task. This approach allows us to classify detected hallucinations as either intrinsic or extrinsic. The paper starts by discussing the field of hallucination detection and introducing several approaches to related work. Then, we introduce the proposed FactAlign approach for hallucination detection and discuss how we can use it to classify hallucinations as either intrinsic or extrinsic. Experiments are carried out to evaluate the proposed method against state-of-the-art methods on the hallucination detection task using the WikiBio GPT-3 hallucination dataset, and on the hallucination type classification task using the XSum hallucination annotations dataset. The experimental results show that our method achieves a 0.889 F1 score for the hallucination detection and 0.825 F1 for the hallucination type classification, without any further training, fine-tuning, or producing multiple samples of the LLM response.

FactAlign: Fact-Level Hallucination Detection and Classification Through Knowledge Graph Alignment

Leveraging large language models (LLMs) as evaluators are becoming a popular trend to replace costly human evaluations. Despite evident benefits, we raise concerns on the reliability of LLM evaluators especially within the context of consistency. We perform extensive assessments on diverse setups of consistency of LLM evaluators and propose methods to improve consistency without the loss of accuracy.

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