Can Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?

• URL: http://arxiv.org/abs/2411.10761v1
• Date: Sat, 16 Nov 2024 09:36:56 GMT
• Title: Can Generic LLMs Help Analyze Child-adult Interactions Involving Children with Autism in Clinical Observation?
• Authors: Tiantian Feng, Anfeng Xu, Rimita Lahiri, Helen Tager-Flusberg, So Hyun Kim, Somer Bishop, Catherine Lord, Shrikanth Narayanan,
• Abstract summary: Large Language Models (LLMs) have shown significant potential in understanding human communication and interaction. We evaluate generic LLMs' ability to analyze child-adult dyadic interactions in a clinically relevant context involving children with ASD.
• Score: 27.730703344144604
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have shown significant potential in understanding human communication and interaction. However, their performance in the domain of child-inclusive interactions, including in clinical settings, remains less explored. In this work, we evaluate generic LLMs' ability to analyze child-adult dyadic interactions in a clinically relevant context involving children with ASD. Specifically, we explore LLMs in performing four tasks: classifying child-adult utterances, predicting engaged activities, recognizing language skills and understanding traits that are clinically relevant. Our evaluation shows that generic LLMs are highly capable of analyzing long and complex conversations in clinical observation sessions, often surpassing the performance of non-expert human evaluators. The results show their potential to segment interactions of interest, assist in language skills evaluation, identify engaged activities, and offer clinical-relevant context for assessments.

Related papers

• TAMA: A Human-AI Collaborative Thematic Analysis Framework Using Multi-Agent LLMs for Clinical Interviews [54.35097932763878]
  Thematic analysis (TA) is a widely used qualitative approach for uncovering latent meanings in unstructured text data. Here, we propose TAMA: A Human-AI Collaborative Thematic Analysis framework using Multi-Agent LLMs for clinical interviews. We demonstrate that TAMA outperforms existing LLM-assisted TA approaches, achieving higher thematic hit rate, coverage, and distinctiveness.
  arXiv  Detail & Related papers  (2025-03-26T15:58:16Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment. We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews. Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Benchmarking LLMs for Mimicking Child-Caregiver Language in Interaction [4.109949110722246]
  LLMs can generate human-like dialogues, yet their ability to simulate early child-adult interactions remains largely unexplored. We found that state-of-the-art LLMs can approximate child- caregiver dialogues at the word and utterance level, but they struggle to reproduce the child and caregiver's discursive patterns, exaggerate alignment, and fail to reach the level of diversity shown by humans.
  arXiv  Detail & Related papers  (2024-12-12T14:43:03Z)
• PALLM: Evaluating and Enhancing PALLiative Care Conversations with Large Language Models [10.258261180305439]
  Large language models (LLMs) offer a new approach to assessing complex communication metrics. LLMs offer the potential to advance the field through integration into passive sensing and just-in-time intervention systems. This study explores LLMs as evaluators of palliative care communication quality, leveraging their linguistic, in-context learning, and reasoning capabilities.
  arXiv  Detail & Related papers  (2024-09-23T16:39:12Z)
• Towards Child-Inclusive Clinical Video Understanding for Autism Spectrum Disorder [27.788204861041553]
  We investigate the use of foundation models across three modalities: speech, video, and text, to analyse child-focused interaction sessions. We evaluate their performance on two tasks with different information granularity: activity recognition and abnormal behavior detection.
  arXiv  Detail & Related papers  (2024-09-20T16:06:46Z)
• Egocentric Speaker Classification in Child-Adult Dyadic Interactions: From Sensing to Computational Modeling [30.099739460287566]
  Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behavior, and sensory processing. One important research area in ASD is evaluating children's behavioral changes over time during treatment. A fundamental aspect of understanding children's behavior in these interactions is automatic speech understanding.
  arXiv  Detail & Related papers  (2024-09-14T07:03:08Z)
• Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance [73.19687314438133]
  We study how reliance is affected by contextual features of an interaction. We find that contextual characteristics significantly affect human reliance behavior. Our results show that calibration and language quality alone are insufficient in evaluating the risks of human-LM interactions.
  arXiv  Detail & Related papers  (2024-07-10T18:00:05Z)
• Can LLMs Understand the Implication of Emphasized Sentences in Dialogue? [64.72966061510375]
  Emphasis is a crucial component in human communication, which indicates the speaker's intention and implication beyond pure text in dialogue. This paper introduces Emphasized-Talk, a benchmark with emphasis-annotated dialogue samples capturing the implications of emphasis. We evaluate various Large Language Models (LLMs), both open-source and commercial, to measure their performance in understanding emphasis.
  arXiv  Detail & Related papers  (2024-06-16T20:41:44Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries [62.32403630651586]
  Large language models (LLMs) have shown the potential to generate accurate clinical text summaries, but still struggle with issues regarding grounding and evaluation. Here, we explore a general mitigation framework using Attribute Structuring (AS), which structures the summary evaluation process. AS consistently improves the correspondence between human annotations and automated metrics in clinical text summarization.
  arXiv  Detail & Related papers  (2024-03-01T21:59:03Z)
• AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator [69.51568871044454]
  We introduce textbfAI Hospital, a framework simulating dynamic medical interactions between emphDoctor as player and NPCs. This setup allows for realistic assessments of LLMs in clinical scenarios. We develop the Multi-View Medical Evaluation benchmark, utilizing high-quality Chinese medical records and NPCs.
  arXiv  Detail & Related papers  (2024-02-15T06:46:48Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling [1.1780706927049207]
  This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents. LLMs present a novel opportunity to augment traditional psychological counseling methods.
  arXiv  Detail & Related papers  (2023-11-12T07:55:39Z)
• Affect Recognition in Conversations Using Large Language Models [9.689990547610664]
  Affect recognition plays a pivotal role in human communication. This study investigates the capacity of large language models (LLMs) to recognise human affect in conversations.
  arXiv  Detail & Related papers  (2023-09-22T14:11:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.