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Methodology
← Models
Deep Learning
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Models
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Large Language Models
2678 directly classified papers
Papers per year
2014: 1
2017: 2
2018: 1
2019: 13
2020: 17
2021: 26
2022: 105
2023: 314
2024: 931
2025: 1268
Papers
CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions
EMNLP 2024
ARM: An Alignment-and-Replacement Module for Chinese Spelling Check Based on LLMs
EMNLP 2024
When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models
EMNLP 2024
LLMs Are Prone to Fallacies in Causal Inference
EMNLP 2024
Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMs
EMNLP 2024
CodeAgent: Autonomous Communicative Agents for Code Review
EMNLP 2024
What Are the Odds? Language Models Are Capable of Probabilistic Reasoning
EMNLP 2024
TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts
EMNLP 2024
Subjective Topic meets LLMs: Unleashing Comprehensive, Reflective and Creative Thinking through the Negation of Negation
EMNLP 2024
Unveiling In-Context Learning: A Coordinate System to Understand Its Working Mechanism
EMNLP 2024
Self-Powered LLM Modality Expansion for Large Speech-Text Models
EMNLP 2024
PATIENT-𝜓: Using Large Language Models to Simulate Patients for Training Mental Health Professionals
EMNLP 2024
Large Language Models Can Self-Correct with Key Condition Verification
EMNLP 2024
Story Morals: Surfacing value-driven narrative schemas using large language models
EMNLP 2024
OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants
EMNLP 2024
Style-Specific Neurons for Steering LLMs in Text Style Transfer
EMNLP 2024
Householder Pseudo-Rotation: A Novel Approach to Activation Editing in LLMs with Direction-Magnitude Perspective
EMNLP 2024
A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations
EMNLP 2024
Consecutive Batch Model Editing with HooK Layers
EMNLP 2024
Structured Optimal Brain Pruning for Large Language Models
EMNLP 2024
How Do Your Code LLMs perform? Empowering Code Instruction Tuning with Really Good Data
EMNLP 2024
Enhancing Training Data Attribution for Large Language Models with Fitting Error Consideration
EMNLP 2024
LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law
EMNLP 2024
Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models
EMNLP 2024
Control Large Language Models via Divide and Conquer
EMNLP 2024
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