Image de couverture de livre

How Far Can We Extract Diverse Perspectives from Large Language Models?

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Hayati, Shirley Anugrah, Lee, Minhwa, Rajagopal, Dheeraj, Kang, Dongyeop
Format:	Preprint
Publié:	2023
Sujets:	Computation and Language
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Confidence Calibration and Rationalization for LLMs via Multi-Agent Deliberation
par: Yang, Ruixin, et autres
Publié: (2024)

Effects of Varying LLM Access on Essay Writing Behavior
par: Christenson, Julia, et autres
Publié: (2026)

Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
par: Hayati, Shirley Anugrah, et autres
Publié: (2024)

Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
par: Lee, Minhwa, et autres
Publié: (2024)

Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation
par: Mooney, James, et autres
Publié: (2025)

Benchmarking Cognitive Biases in Large Language Models as Evaluators
par: Koo, Ryan, et autres
Publié: (2023)

Scalable Influence and Fact Tracing for Large Language Model Pretraining
par: Chang, Tyler A., et autres
Publié: (2024)

Under the Surface: Tracking the Artifactuality of LLM-Generated Data
par: Das, Debarati, et autres
Publié: (2024)

ScholaWrite: A Dataset of End-to-End Scholarly Writing Process
par: Le, Khanh Chi, et autres
Publié: (2025)

Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
par: Lin, Zhengkai, et autres
Publié: (2024)

Vulnerability Detection with Code Language Models: How Far Are We?
par: Ding, Yangruibo, et autres
Publié: (2024)

LLM Compression: How Far Can We Go in Balancing Size and Performance?
par: Sk, Sahil, et autres
Publié: (2025)

MotiveBench: How Far Are We From Human-Like Motivational Reasoning in Large Language Models?
par: Yong, Xixian, et autres
Publié: (2025)

Steering off Course: Reliability Challenges in Steering Language Models
par: Da Silva, Patrick Queiroz, et autres
Publié: (2025)

Large Language Models for Predictive Analysis: How Far Are They?
par: Chen, Qin, et autres
Publié: (2025)

How Far Are We from Optimal Reasoning Efficiency?
par: Gao, Jiaxuan, et autres
Publié: (2025)

Model Editing for LLMs4Code: How Far are We?
par: Li, Xiaopeng, et autres
Publié: (2024)

LLMs for Relational Reasoning: How Far are We?
par: Li, Zhiming, et autres
Publié: (2024)

How Far Are We from Generating Missing Modalities with Foundation Models?
par: Ke, Guanzhou, et autres
Publié: (2025)

Which Modality should I use -- Text, Motif, or Image? : Understanding Graphs with Large Language Models
par: Das, Debarati, et autres
Publié: (2023)

How Far Are We from Genuinely Useful Deep Research Agents?
par: Zhang, Dingling, et autres
Publié: (2025)

How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code
par: Lee, Seonghyeon, et autres
Publié: (2025)

How Far Are We From AGI: Are LLMs All We Need?
par: Feng, Tao, et autres
Publié: (2024)

Structure Liberates: How Constrained Sensemaking Produces More Novel Research Output
par: Mooney, James, et autres
Publié: (2026)

PixelWorld: How Far Are We from Perceiving Everything as Pixels?
par: Lyu, Zhiheng, et autres
Publié: (2025)

How Far Are We from Intelligent Visual Deductive Reasoning?
par: Zhang, Yizhe, et autres
Publié: (2024)

Def-DTS: Deductive Reasoning for Open-domain Dialogue Topic Segmentation
par: Lee, Seungmin, et autres
Publié: (2025)

FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?
par: Raza, Shaina, et autres
Publié: (2024)

How Far Are We? Systematic Evaluation of LLMs vs. Human Experts in Mathematical Contest in Modeling
par: Liu, Yuhang, et autres
Publié: (2026)

Tracing How Annotators Think: Augmenting Preference Judgments with Reading Processes
par: de Langis, Karin, et autres
Publié: (2025)

Developing Safe and Responsible Large Language Model : Can We Balance Bias Reduction and Language Understanding in Large Language Models?
par: Raza, Shaina, et autres
Publié: (2024)

R-Horizon: How Far Can Your Large Reasoning Model Really Go in Breadth and Depth?
par: Lu, Yi, et autres
Publié: (2025)

SelectLLM: Can LLMs Select Important Instructions to Annotate?
par: Parkar, Ritik Sachin, et autres
Publié: (2024)

Reasoning Runtime Behavior of a Program with LLM: How Far Are We?
par: Chen, Junkai, et autres
Publié: (2024)

Entailment Semantics Can Be Extracted from an Ideal Language Model
par: Merrill, William, et autres
Publié: (2022)

Can We Edit Multimodal Large Language Models?
par: Cheng, Siyuan, et autres
Publié: (2023)

Can Large Language Models Reliably Extract Physiology Index Values from Coronary Angiography Reports?
par: Morgado, Sofia, et autres
Publié: (2026)

Can We Predict Performance of Large Models across Vision-Language Tasks?
par: Zhao, Qinyu, et autres
Publié: (2024)

Align to Structure: Aligning Large Language Models with Structural Information
par: Kim, Zae Myung, et autres
Publié: (2025)

Can We Use Large Language Models to Fill Relevance Judgment Holes?
par: Abbasiantaeb, Zahra, et autres
Publié: (2024)