A multimodal LLM pipeline with Wav2Vec audio and WHO-based Q&A knowledge injection reports small improvements on DAIC-WOZ, but the fusion adds nothing over audio-only and the baseline is cherry-picked.
PsychoLex: Unveiling the Psychological Mind of Large Language Models
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abstract
This paper explores the intersection of psychology and artificial intelligence through the development and evaluation of specialized Large Language Models (LLMs). We introduce PsychoLex, a suite of resources designed to enhance LLMs' proficiency in psychological tasks in both Persian and English. Key contributions include the PsychoLexQA dataset for instructional content and the PsychoLexEval dataset for rigorous evaluation of LLMs in complex psychological scenarios. Additionally, we present the PsychoLexLLaMA model, optimized specifically for psychological applications, demonstrating superior performance compared to general-purpose models. The findings underscore the potential of tailored LLMs for advancing psychological research and applications, while also highlighting areas for further refinement. This research offers a foundational step towards integrating LLMs into specialized psychological domains, with implications for future advancements in AI-driven psychological practice.
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cs.HC 1years
2025 1verdicts
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Large Language Models for Depression Recognition in Spoken Language Integrating Psychological Knowledge
A multimodal LLM pipeline with Wav2Vec audio and WHO-based Q&A knowledge injection reports small improvements on DAIC-WOZ, but the fusion adds nothing over audio-only and the baseline is cherry-picked.