Copertina

Do LLMs Understand Why We Write Diaries? A Method for Purpose Extraction and Clustering

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Goloviznina, Valeriya, Sergeev, Alexander, Melnichenko, Mikhail, Kotelnikov, Evgeny
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Talking to Data: Designing Smart Assistants for Humanities Databases
di: Sergeev, Alexander, et al.
Pubblicazione: (2025)

I've got the "Answer"! Interpretation of LLMs Hidden States in Question Answering
di: Goloviznina, Valeriya, et al.
Pubblicazione: (2024)

Optimizing Multimodal Language Models through Attention-based Interpretability
di: Sergeev, Alexander, et al.
Pubblicazione: (2025)

Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback
di: Skopin, Egor, et al.
Pubblicazione: (2026)

Can LLM Teams Play What? Where? When?
di: Kotelnikova, Anastasia, et al.
Pubblicazione: (2026)

Why Are We Lonely? Leveraging LLMs to Measure and Understand Loneliness in Caregivers and Non-caregivers
di: Kim, Michelle Damin, et al.
Pubblicazione: (2026)

Skill-LLM: Repurposing General-Purpose LLMs for Skill Extraction
di: Herandi, Amirhossein, et al.
Pubblicazione: (2024)

How LLMs Cite and Why It Matters: A Cross-Model Audit of Reference Fabrication in AI-Assisted Academic Writing and Methods to Detect Phantom Citations
di: Naser, MZ
Pubblicazione: (2026)

Why Do Large Language Models (LLMs) Struggle to Count Letters?
di: Fu, Tairan, et al.
Pubblicazione: (2024)

Structure-Enhanced Protein Instruction Tuning: Towards General-Purpose Protein Understanding with LLMs
di: Wu, Wei, et al.
Pubblicazione: (2024)

When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs
di: Seleznyov, Mikhail, et al.
Pubblicazione: (2025)

How Well Do LLMs Imitate Human Writing Style?
di: Jemama, Rebira, et al.
Pubblicazione: (2025)

Do We Know What LLMs Don't Know? A Study of Consistency in Knowledge Probing
di: Zhao, Raoyuan, et al.
Pubblicazione: (2025)

Why Do We Laugh? Annotation and Taxonomy Generation for Laughable Contexts in Spontaneous Text Conversation
di: Inoue, Koji, et al.
Pubblicazione: (2025)

Why We Build Local Large Language Models: An Observational Analysis from 35 Japanese and Multilingual LLMs
di: Saito, Koshiro, et al.
Pubblicazione: (2024)

An Expert-grounded benchmark of General Purpose LLMs in LCA
di: Donaldson, Artur, et al.
Pubblicazione: (2025)

General LLMs as Instructors for Domain-Specific LLMs: A Sequential Fusion Method to Integrate Extraction and Editing
di: Zhang, Xin, et al.
Pubblicazione: (2024)

Using LLMs for the Extraction and Normalization of Product Attribute Values
di: Brinkmann, Alexander, et al.
Pubblicazione: (2024)

Information Extraction from Electricity Invoices with General-Purpose Large Language Models
di: Gómez, Javier, et al.
Pubblicazione: (2026)

How Well Do LLMs Understand Tunisian Arabic?
di: Mahdi, Mohamed
Pubblicazione: (2025)

Do Methods to Jailbreak and Defend LLMs Generalize Across Languages?
di: Atil, Berk, et al.
Pubblicazione: (2025)

CUTE: Measuring LLMs' Understanding of Their Tokens
di: Edman, Lukas, et al.
Pubblicazione: (2024)

Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?
di: Zverev, Egor, et al.
Pubblicazione: (2024)

When Do LLMs Admit Their Mistakes? Understanding The Role Of Model Belief In Retraction
di: Yang, Yuqing, et al.
Pubblicazione: (2025)

Do as We Do, Not as You Think: the Conformity of Large Language Models
di: Weng, Zhiyuan, et al.
Pubblicazione: (2025)

How Well Do LLMs Understand Drug Mechanisms? A Knowledge + Reasoning Evaluation Dataset
di: Mohan, Sunil, et al.
Pubblicazione: (2025)

GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection?
di: Jin, Yiping, et al.
Pubblicazione: (2024)

A General-Purpose Device for Interaction with LLMs
di: Xu, Jiajun, et al.
Pubblicazione: (2024)

Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet
di: Atil, Berk, et al.
Pubblicazione: (2025)

Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models
di: Polak, Maciej P., et al.
Pubblicazione: (2023)

Help Me Write a Story: Evaluating LLMs' Ability to Generate Writing Feedback
di: Rashkin, Hannah, et al.
Pubblicazione: (2025)

Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
di: Zou, Chenye, et al.
Pubblicazione: (2025)

How Do Humans Write Code? Large Models Do It the Same Way Too
di: Li, Long, et al.
Pubblicazione: (2024)

Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
di: Alaswad, Feisal, et al.
Pubblicazione: (2026)

Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks
di: Chang, Ting-Yun, et al.
Pubblicazione: (2023)

Structured Outputs Enable General-Purpose LLMs to be Medical Experts
di: Guo, Guangfu, et al.
Pubblicazione: (2025)

Do LLMs Recognize me, When I is not me: Assessment of LLMs Understanding of Turkish Indexical Pronouns in Indexical Shift Contexts
di: Oğuz, Metehan, et al.
Pubblicazione: (2024)

Beyond Early-Token Bias: Model-Specific and Language-Specific Position Effects in Multilingual LLMs
di: Menschikov, Mikhail, et al.
Pubblicazione: (2025)

Leveraging LLMs For Turkish Skill Extraction
di: İltüzer, Ezgi Arslan, et al.
Pubblicazione: (2026)

Why do LLMs attend to the first token?
di: Barbero, Federico, et al.
Pubblicazione: (2025)