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Think you have Solved Question Answering? Try ARC, the AI2 Reasoning
Challenge

Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

14 March 2018

Ashish Sabharwal

Carissa Schoenick

Oyvind Tafjord

ArXiv (abs)PDF HTML

Papers citing "Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge"

50 / 1,907 papers shown

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From Score Distributions to Balance: Plug-and-Play Mixture-of-Experts Routing

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Pretraining with hierarchical memories: separating long-tail and common knowledge

Pretraining with hierarchical memories: separating long-tail and common knowledge

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Michael Kirchhof

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LLM DNA: Tracing Model Evolution via Functional Representations

LLM DNA: Tracing Model Evolution via Functional Representations

124

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Query Circuits: Explaining How Language Models Answer User Prompts

Query Circuits: Explaining How Language Models Answer User Prompts

154

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29 Sep 2025

Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models

Reasoning or Retrieval? A Study of Answer Attribution on Large Reasoning Models

141

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29 Sep 2025

Conda: Column-Normalized Adam for Training Large Language Models Faster

Conda: Column-Normalized Adam for Training Large Language Models Faster

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ChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs Inference

ChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs Inference

164

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Tequila: Trapping-free Ternary Quantization for Large Language Models

Tequila: Trapping-free Ternary Quantization for Large Language Models

237

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Sequential Diffusion Language Models

Sequential Diffusion Language Models

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104

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Beyond Benchmarks: Understanding Mixture-of-Experts Models through Internal Mechanisms

Beyond Benchmarks: Understanding Mixture-of-Experts Models through Internal Mechanisms

55

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Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning

Winning the Pruning Gamble: A Unified Approach to Joint Sample and Token Pruning for Efficient Supervised Fine-Tuning

...

147

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0

28 Sep 2025

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions

368

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Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Models

Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Models

97

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A2D: Any-Order, Any-Step Safety Alignment for Diffusion Language Models

138

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27 Sep 2025

MoE-PHDS: One MoE checkpoint for flexible runtime sparsity

MoE-PHDS: One MoE checkpoint for flexible runtime sparsity

Soheil Zibakhsh

Mohammad Samragh Razlighi

Mehrdad Farajtabar

96

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Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review

Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review

124

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Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization

Bridging the Gap Between Promise and Performance for Microscaling FP4 Quantization

Vage Egiazarian

Roberto L. Castro

Denis Kuznedelev

Andrei Panferov

...

Alexandre Marques

Torsten Hoefler

228

1

0

27 Sep 2025

Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models

Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models

121

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Multiplayer Nash Preference Optimization

Multiplayer Nash Preference Optimization

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137

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Train Once, Answer All: Many Pretraining Experiments for the Cost of One

Train Once, Answer All: Many Pretraining Experiments for the Cost of One

Sebastian Bordt

Martin Pawelczyk

175

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27 Sep 2025

SDQ-LLM: Sigma-Delta Quantization for 1-bit LLMs of any size

SDQ-LLM: Sigma-Delta Quantization for 1-bit LLMs of any size

107

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27 Sep 2025

PT$^2$-LLM: Post-Training Ternarization for Large Language Models

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207

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Beyond Outliers: A Study of Optimizers Under Quantization

Beyond Outliers: A Study of Optimizers Under Quantization

Georgios Vlassis

Alexandra Volkova

Torsten Hoefler

206

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DOoM: Difficult Olympiads of Math

DOoM: Difficult Olympiads of Math

Nikolay Kompanets

Aleksandr Nikolich

254

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Thinking in Many Modes: How Composite Reasoning Elevates Large Language Model Performance with Limited Data

Thinking in Many Modes: How Composite Reasoning Elevates Large Language Model Performance with Limited Data

Saisubramaniam Gopalakrishnan

90

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26 Sep 2025

What Matters More For In-Context Learning under Matched Compute Budgets: Pretraining on Natural Text or Incorporating Targeted Synthetic Examples?

What Matters More For In-Context Learning under Matched Compute Budgets: Pretraining on Natural Text or Incorporating Targeted Synthetic Examples?

98

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26 Sep 2025

Stochastic activations

Stochastic activations

Gergely Szilvasy

Sainbayar Sukhbaatar

Gabriel Synnaeve

Pierre-Emmanuel Mazaré

268

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26 Sep 2025

Tiny-QMoE

24

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26 Sep 2025

Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts

Elastic MoE: Unlocking the Inference-Time Scalability of Mixture-of-Experts

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85

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26 Sep 2025

Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning

Erase or Hide? Suppressing Spurious Unlearning Neurons for Robust Unlearning

112

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COSPADI: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning

COSPADI: Compressing LLMs via Calibration-Guided Sparse Dictionary Learning

Dmitriy Shopkhoev

Magauiya Zhussip

Stamatios Lefkimmiatis

186

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MindCraft: How Concept Trees Take Shape In Deep Models

MindCraft: How Concept Trees Take Shape In Deep Models

102

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Towards Generalizable Implicit In-Context Learning with Attention Routing

Towards Generalizable Implicit In-Context Learning with Attention Routing

141

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Context Parametrization with Compositional Adapters

Context Parametrization with Compositional Adapters

123

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Meta-Awareness Enhances Reasoning Models: Self-Alignment Reinforcement Learning

Meta-Awareness Enhances Reasoning Models: Self-Alignment Reinforcement Learning

199

1

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Lightweight error mitigation strategies for post-training N:M activation sparsity in LLMs

Lightweight error mitigation strategies for post-training N:M activation sparsity in LLMs

Kristina Kazistova

Ekaterina Galaeva

Alina Kostromina

Vladimir Smirnov

137

0

0

26 Sep 2025

SBFA: Single Sneaky Bit Flip Attack to Break Large Language Models

SBFA: Single Sneaky Bit Flip Attack to Break Large Language Models

65

3

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IIET: Efficient Numerical Transformer via Implicit Iterative Euler Method

IIET: Efficient Numerical Transformer via Implicit Iterative Euler Method

179

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Rethinking RoPE Scaling in Quantized LLM: Theory, Outlier, and Channel-Band Analysis with Weight Rescaling

Rethinking RoPE Scaling in Quantized LLM: Theory, Outlier, and Channel-Band Analysis with Weight Rescaling

112

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26 Sep 2025

Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data

Front-Loading Reasoning: The Synergy between Pretraining and Post-Training Data

Syeda Nahida Akter

Shrimai Prabhumoye

Mohammad Shoeybi

Bryan Catanzaro

AIFin LRM AI4CE

120

6

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26 Sep 2025

JGU Mainz's Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA

JGU Mainz's Submission to the WMT25 Shared Task on LLMs with Limited Resources for Slavic Languages: MT and QA

Hossain Shaikh Saadi

Mario Sanz-Guerrero

Katharina von der Wense

83

1

0

26 Sep 2025

Blockwise Hadamard high-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning

Blockwise Hadamard high-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning

153

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25 Sep 2025

Predicting LLM Reasoning Performance with Small Proxy Model

Predicting LLM Reasoning Performance with Small Proxy Model

270

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25 Sep 2025

Painless Activation Steering: An Automated, Lightweight Approach for Post-Training Large Language Models

Painless Activation Steering: An Automated, Lightweight Approach for Post-Training Large Language Models

233

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Expanding Reasoning Potential in Foundation Model by Learning Diverse Chains of Thought Patterns

Expanding Reasoning Potential in Foundation Model by Learning Diverse Chains of Thought Patterns

210

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Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say

Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say

Jacob Fein-Ashley

Rajgopal Kannan

Viktor Prasanna

175

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On Code-Induced Reasoning in LLMs

On Code-Induced Reasoning in LLMs

Daphne Ippolito

157

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SFT Doesn't Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs

SFT Doesn't Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs

Arvind Srinivasan

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350

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Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing

Detoxifying Large Language Models via Autoregressive Reward Guided Representation Editing

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