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Methodology
← Optimization & Theory
Machine Learning
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Optimization & Theory
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Learning Theory
5312 directly classified papers
Papers per year
2001: 1
2002: 16
2003: 16
2004: 15
2005: 17
2006: 30
2007: 32
2008: 32
2009: 34
2010: 66
2011: 76
2012: 74
2013: 94
2014: 115
2015: 123
2016: 128
2017: 185
2018: 219
2019: 390
2020: 466
2021: 640
2022: 664
2023: 799
2024: 688
2025: 307
2026: 85
Papers
Towards a Holistic and Automated Evaluation Framework for Multi-Level Comprehension of LLMs in Book-Length Contexts
EMNLP 2025
Adaptively profiling models with task elicitation
EMNLP 2025
Do LLMs Adhere to Label Definitions? Examining Their Receptivity to External Label Definitions
EMNLP 2025
Investigating How Pre-training Data Leakage Affects Models’ Reproduction and Detection Capabilities
EMNLP 2025
The Emperor’s New Reasoning: Format Imitation Overshadows Genuine Mathematical Understanding in SFT
EMNLP 2025
Tokenization and Representation Biases in Multilingual Models on Dialectal NLP Tasks
EMNLP 2025
Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models
EMNLP 2025
Identifying Pre-training Data in LLMs: A Neuron Activation-Based Detection Framework
EMNLP 2025
LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning
EMNLP 2025
Evaluating the Evaluators: Are readability metrics good measures of readability?
EMNLP 2025
From Input Perception to Predictive Insight: Modeling Model Blind Spots Before They Become Errors
EMNLP 2025
Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic
EMNLP 2025
Circuit Complexity Bounds for RoPE-based Transformer Architecture
EMNLP 2025
Mind the Gap: How BabyLMs Learn Filler-Gap Dependencies
EMNLP 2025
Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Making
EMNLP 2025
Axioms for AI Alignment from Human Feedback
AAAI 2025
Reason to Rote: Rethinking Memorization in Reasoning
EMNLP 2025
The Bandit Whisperer: Communication Learning for Restless Bandits
AAAI 2025
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits
AAAI 2025
Cost-Aware Near-Optimal Policy Learning
AAAI 2025
Language models can learn implicit multi-hop reasoning, but only if they have lots of training data
EMNLP 2025
LLMs cannot spot math errors, even when allowed to peek into the solution
EMNLP 2025
Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving
EMNLP 2025
How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark
EMNLP 2025
Can Large Language Models Win the International Mathematical Games?
EMNLP 2025
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