Fine-Tuned Multi-Agent Framework for Detecting OCEAN in Life Narratives

• URL: http://arxiv.org/abs/2607.12215v1
• Date: Mon, 13 Jul 2026 23:26:16 GMT
• Title: Fine-Tuned Multi-Agent Framework for Detecting OCEAN in Life Narratives
• Abstract summary: Large language models (LLMs) offer new opportunities by processing extensive textual contexts.<n>LLMs can induce latent "personality-like" biases, making single-model inferences inconsistent.<n>We propose a fine-tuned multi-agent framework for detecting OCEAN personality traits.
• Score: 0.3779860024918729
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately assessing personality from text is challenging because traits are latent, context-dependent, and often subtly expressed across long narratives. Large language models (LLMs) offer new opportunities by processing extensive textual contexts, but pretraining of these models can induce latent "personality-like" biases, making single-model inferences inconsistent. We propose a fine-tuned multi-agent framework for detecting OCEAN personality traits, in which sub-agents are conditioned to adopt high, low, or neutral perspectives for each trait through masked language modeling (MLM) and psychometric supervision. A judge LLM aggregates and compares sub-agent outputs to generate final trait predictions, capturing multiple complementary perspectives while mitigating individual model biases. We evaluate the framework on life narrative dataset through quantitative and qualitative experiments, including baselines, ablations, and inference quality analyses. Our approach offers a scalable and interpretable method for text-based personality inference, highlighting the benefits of multi-agent reasoning grounded in psychometric supervision.

Related papers

• Beyond Standard Benchmarks: A Systematic Audit of Vision-Language Model's Robustness to Natural Semantic Variation Across Diverse Tasks [11.064940886724257]
  This paper presents a systematic evaluation framework for vision-language models (VLMs) under natural adversarial scenarios.<n>We measure the natural adversarial performance of selected VLMs for zero-shot image classification, semantic segmentation, and visual question answering.<n>Our analysis reveals that robust CLIP models can amplify natural adversarial vulnerabilities, and CLIP models significantly reduce performance for natural language-induced adversarial examples.
  arXiv  Detail & Related papers  (2026-04-06T06:48:32Z)
• How Do LLM-Generated Texts Impact Term-Based Retrieval Models? [76.92519309816008]
  This paper investigates the influence of large language models (LLMs) on term-based retrieval models.<n>Our linguistic analysis reveals that LLM-generated texts exhibit smoother high-frequency and steeper low-frequency Zipf slopes.<n>Our study further explores whether term-based retrieval models demonstrate source bias, concluding that these models prioritize documents whose term distributions closely correspond to those of the queries.
  arXiv  Detail & Related papers  (2025-08-25T06:43:27Z)
• Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems [3.011820285006942]
  This study proposes a new multi-perspective approach using soft labels to encourage the development of perspective aware models.<n>We conduct an analysis across diverse subjective text classification tasks, including hate speech, irony, abusive language, and stance detection.<n>Results show that the multi-perspective approach better approximates human label distributions, as measured by Jensen-Shannon Divergence (JSD)<n>Our approach exhibits lower confidence in tasks like irony and stance detection, likely due to the inherent subjectivity present in the texts.
  arXiv  Detail & Related papers  (2025-06-25T07:53:36Z)
• Personality Prediction from Life Stories using Language Models [12.851871085845499]
  In this study, we address the challenge of modeling long narrative interview where each exceeds 2000 tokens so as to predict Five-Factor Model (FFM) personality traits.<n>We propose a two-step approach: first, we extract contextual embeddings using sliding-window fine-tuning of pretrained language models; then, we apply Recurrent Neural Networks (RNNs) with attention mechanisms to integrate long-range dependencies and enhance interpretability.
  arXiv  Detail & Related papers  (2025-06-24T02:39:06Z)
• IDA-Bench: Evaluating LLMs on Interactive Guided Data Analysis [60.32962597618861]
  IDA-Bench is a novel benchmark evaluating large language models in multi-round interactive scenarios.<n>Agent performance is judged by comparing its final numerical output to the human-derived baseline.<n>Even state-of-the-art coding agents (like Claude-3.7-thinking) succeed on 50% of the tasks, highlighting limitations not evident in single-turn tests.
  arXiv  Detail & Related papers  (2025-05-23T09:37:52Z)
• Beyond Self-Reports: Multi-Observer Agents for Personality Assessment in Large Language Models [2.7010154811483167]
  This paper proposes a novel multi-observer framework for personality trait assessments in LLM agents.<n>Instead of relying on self-assessments, we employ multiple observer agents.<n>We show that these observer-report ratings align more closely with human judgments than traditional self-assessments.
  arXiv  Detail & Related papers  (2025-04-11T10:03:55Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• Generative Judge for Evaluating Alignment [84.09815387884753]
  We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
  arXiv  Detail & Related papers  (2023-10-09T07:27:15Z)
• Large Language Models are Diverse Role-Players for Summarization Evaluation [82.31575622685902]
  A document summary's quality can be assessed by human annotators on various criteria, both objective ones like grammar and correctness, and subjective ones like informativeness, succinctness, and appeal. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to adequately capture the above dimensions. We propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects.
  arXiv  Detail & Related papers  (2023-03-27T10:40:59Z)
• A Unified Understanding of Deep NLP Models for Text Classification [88.35418976241057]
  We have developed a visual analysis tool, DeepNLPVis, to enable a unified understanding of NLP models for text classification. The key idea is a mutual information-based measure, which provides quantitative explanations on how each layer of a model maintains the information of input words in a sample. A multi-level visualization, which consists of a corpus-level, a sample-level, and a word-level visualization, supports the analysis from the overall training set to individual samples.
  arXiv  Detail & Related papers  (2022-06-19T08:55:07Z)
• An Investigation of Language Model Interpretability via Sentence Editing [5.492504126672887]
  We re-purpose a sentence editing dataset as a testbed for interpretability of pre-trained language models (PLMs) This enables us to conduct a systematic investigation on an array of questions regarding PLMs' interpretability. The investigation generates new insights, for example, contrary to the common understanding, we find that attention weights correlate well with human rationales.
  arXiv  Detail & Related papers  (2020-11-28T00:46:43Z)

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.