The Pragmatic Persona: Discovering LLM Persona through Bridging Inference
- URL: http://arxiv.org/abs/2604.24079v1
- Date: Mon, 27 Apr 2026 06:01:47 GMT
- Title: The Pragmatic Persona: Discovering LLM Persona through Bridging Inference
- Abstract summary: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue.<n>We propose a novel framework that interprets dialogue through bridging inference.<n>Our approach captures latent semantic links that govern how LLMs organize meaning across turns.
- Score: 6.671933768892354
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Language Models (LLMs) reveal inherent and distinctive personas through dialogue. However, most existing persona discovery approaches rely on surface-level lexical or stylistic cues, treating dialogue as a flat sequence of tokens and failing to capture the deeper discourse-level structures that sustain persona consistency. To address this limitation, we propose a novel analytical framework that interprets LLM dialogue through bridging inference -- implicit conceptual relations that connect utterances via shared world knowledge and discourse coherence. By modeling these relations as structured knowledge graphs, our approach captures latent semantic links that govern how LLMs organize meaning across turns, enabling persona discovery at the level of discourse coherence rather than surface realizations. Experimental results across multiple reasoning backbones and target LLMs, ranging from small-scale models to 80B-parameter systems, demonstrate that bridging-inference graphs yield significantly stronger semantic coherence and more stable persona identification than frequency or style-based baselines. These results show that persona traits are consistently encoded in the structural organization of discourse rather than isolated lexical patterns. This work presents a systematic framework for probing, extracting, and visualizing latent LLM personas through the lens of Cognitive Discourse Theory, bridging computational linguistics, cognitive semantics, and persona reasoning in large language models. Codes are available at https://github.com/JiSoo-Yang/Persona_Bridging.git
Related papers
- Shaping Schema via Language Representation as the Next Frontier for LLM Intelligence Expanding [88.11781604392606]
This paper defines language representation as the linguistic and symbolic constructs used to map and model the real world.<n>We argue that shaping schemas through advanced language representation is the next frontier for expanding Large Language Models.
arXiv Detail & Related papers (2026-05-10T02:42:29Z) - Individualized Cognitive Simulation in Large Language Models: Evaluating Different Cognitive Representation Methods [36.28947625215469]
Large language models (LLMs) convincingly mimic surface-level human behavior such as role-play.<n>But their ability to simulate deeper individualized cognitive processes remains poorly understood.<n>We evaluate different cognitive representations, e.g., linguistic features, concept mappings, and profile-based information.
arXiv Detail & Related papers (2025-10-23T06:18:15Z) - From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models [66.36007274540113]
Multimodal Large Language Models (MLLMs) strive to achieve a profound, human-like understanding of and interaction with the physical world.<n>They often exhibit a shallow and incoherent integration when acquiring information (Perception) and conducting reasoning (Cognition)<n>This survey introduces a novel and unified analytical framework: From Perception to Cognition"
arXiv Detail & Related papers (2025-09-29T18:25:40Z) - Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth [21.092167028989632]
Drivelology is a linguistic phenomenon characterised by "nonsense with depth"<n>We construct a benchmark dataset of over 1,200+ meticulously curated and diverse examples across English, Mandarin, Spanish, French, Japanese, and Korean.<n>We find that current large language models (LLMs) consistently fail to grasp the layered semantics of Drivelological text.
arXiv Detail & Related papers (2025-09-04T03:58:55Z) - How LLMs Comprehend Temporal Meaning in Narratives: A Case Study in Cognitive Evaluation of LLMs [13.822169295436177]
We investigate how large language models (LLMs) process the temporal meaning of linguistic aspect in narratives that were previously used in human studies.<n>Our findings show that LLMs over-rely on prototypicality, produce inconsistent aspectual judgments, and struggle with causal reasoning derived from aspect.<n>These results suggest that LLMs process aspect fundamentally differently from humans and lack robust narrative understanding.
arXiv Detail & Related papers (2025-07-18T18:28:35Z) - Semantic Mastery: Enhancing LLMs with Advanced Natural Language Understanding [0.0]
The paper discusses state-of-the-art methodologies that advance large language models (LLMs) with more advanced NLU techniques.<n>We analyze the use of structured knowledge graphs, retrieval-augmented generation (RAG), and fine-tuning strategies that match models with human-level understanding.
arXiv Detail & Related papers (2025-04-01T04:12:04Z) - An LLM Feature-based Framework for Dialogue Constructiveness Assessment [8.87747076871578]
Research on dialogue constructiveness assessment focuses on (i) analysing conversational factors that influence individuals to take specific actions, win debates, change their perspectives or broaden their open-mindedness and (ii) predicting constructiveness outcomes following dialogues for such use cases.
These objectives can be achieved by training either interpretable feature-based models or neural models such as pre-trained language models.
We propose an LLM feature-based framework for dialogue constructiveness assessment that combines the strengths of feature-based and neural approaches.
arXiv Detail & Related papers (2024-06-20T22:10:52Z) - Detecting Any Human-Object Interaction Relationship: Universal HOI
Detector with Spatial Prompt Learning on Foundation Models [55.20626448358655]
This study explores the universal interaction recognition in an open-world setting through the use of Vision-Language (VL) foundation models and large language models (LLMs)
Our design includes an HO Prompt-guided Decoder (HOPD), facilitates the association of high-level relation representations in the foundation model with various HO pairs within the image.
For open-category interaction recognition, our method supports either of two input types: interaction phrase or interpretive sentence.
arXiv Detail & Related papers (2023-11-07T08:27:32Z) - CoVLM: Composing Visual Entities and Relationships in Large Language
Models Via Communicative Decoding [66.52659447360104]
CoVLM can guide the LLM to explicitly compose visual entities and relationships among the text.
We propose CoVLM, which can guide the LLM to explicitly compose visual entities and relationships among the text.
arXiv Detail & Related papers (2023-11-06T18:59:44Z) - Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds [59.71218039095155]
We evaluate language understanding capacities on simple inference tasks that most humans find trivial.
We target (i) grammatically-specified entailments, (ii) premises with evidential adverbs of uncertainty, and (iii) monotonicity entailments.
The models exhibit moderate to low performance on these evaluation sets.
arXiv Detail & Related papers (2023-05-24T06:41:09Z) - Cue-CoT: Chain-of-thought Prompting for Responding to In-depth Dialogue
Questions with LLMs [59.74002011562726]
We propose a novel linguistic cue-based chain-of-thoughts (textitCue-CoT) to provide a more personalized and engaging response.
We build a benchmark with in-depth dialogue questions, consisting of 6 datasets in both Chinese and English.
Empirical results demonstrate our proposed textitCue-CoT method outperforms standard prompting methods in terms of both textithelpfulness and textitacceptability on all datasets.
arXiv Detail & Related papers (2023-05-19T16:27:43Z)
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.