Evidence map›Paper›PMID 40596002›Full record

ArticleScientific reports2025

Exploring the potential of lightweight large language models for AI-based mental health counselling task: a novel comparative study.

Ritesh Maurya, Nikhil Rajput, M G Diviit, Satyajit Mahapatra, Manish Kumar Ojha

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ritesh MauryaDepartment of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, 273010, India.
Nikhil RajputDepartment of Artificial Intelligence, Amity University, Noida, 201303, India.
M G DiviitDepartment of Artificial Intelligence, Amity University, Noida, 201303, India.
Satyajit MahapatraDepartment of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, India. satyajit.mahapatra@manipal.edu.
Manish Kumar OjhaDepartment of Artificial Intelligence, Amity University, Noida, 201303, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In recent years, Transformer-based large language models (LLMs) have significantly improved upon their text generation capability. Mental health is a serious concern that can be addressed using LLM-based automated mental health counselors. These systems can provide empathetic responses to individuals in need while considering the negative beliefs, stigma, and taboos associated with mental health issues. Considering the large size of these LLMs makes it difficult to deploy these automated counselors on low cost/resource devices such as edge devices. Therefore, the motivation of the present study to analyze the effectiveness of lightweight LLMs in the development of automated mental health counseling systems. In this study, lightweight open source LLMs such as Google's T5

Indexed as

Artificial IntelligenceCounselingLanguageMental HealthHumansLarge Language ModelsArtificial intelligenceCounselingFine-tuningLarge language modelMental health

Identifiers

PMID40596002
PMCPMC12214774

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.