Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Fuente:
arXiv
Saved in:
| Main Authors: | Levy, Mosh, Jacoby, Alon, Goldberg, Yoav |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
State over Tokens: Characterizing the Role of Reasoning Tokens
by: Levy, Mosh, et al.
Published: (2025)
by: Levy, Mosh, et al.
Published: (2025)
Humans Perceive Wrong Narratives from AI Reasoning Texts
by: Levy, Mosh, et al.
Published: (2025)
by: Levy, Mosh, et al.
Published: (2025)
Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature
by: Katz, Uri, et al.
Published: (2024)
by: Katz, Uri, et al.
Published: (2024)
Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds
by: Basmov, Victoria, et al.
Published: (2023)
by: Basmov, Victoria, et al.
Published: (2023)
The Impact of Reasoning Step Length on Large Language Models
by: Jin, Mingyu, et al.
Published: (2024)
by: Jin, Mingyu, et al.
Published: (2024)
Is Large Language Model Performance on Reasoning Tasks Impacted by Different Ways Questions Are Asked?
by: Song, Seok Hwan, et al.
Published: (2025)
by: Song, Seok Hwan, et al.
Published: (2025)
More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
by: Chen, Evan, et al.
Published: (2025)
by: Chen, Evan, et al.
Published: (2025)
Compared to What? Baselines and Metrics for Counterfactual Prompting
by: Yang, Zihao, et al.
Published: (2026)
by: Yang, Zihao, et al.
Published: (2026)
MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents
by: Wolfson, Tomer, et al.
Published: (2025)
by: Wolfson, Tomer, et al.
Published: (2025)
Do Thinking Tokens Help or Trap? Towards More Efficient Large Reasoning Model
by: Ding, Bowen, et al.
Published: (2025)
by: Ding, Bowen, et al.
Published: (2025)
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models
by: Wu, Canhui, et al.
Published: (2025)
by: Wu, Canhui, et al.
Published: (2025)
Counting Ability of Large Language Models and Impact of Tokenization
by: Zhang, Xiang, et al.
Published: (2024)
by: Zhang, Xiang, et al.
Published: (2024)
HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark
by: Cohen, Amir DN, et al.
Published: (2025)
by: Cohen, Amir DN, et al.
Published: (2025)
Length-MAX Tokenizer for Language Models
by: Dong, Dong, et al.
Published: (2025)
by: Dong, Dong, et al.
Published: (2025)
More Thinking, More Bias: Length-Driven Position Bias in Reasoning Models
by: Wang, Xiao
Published: (2026)
by: Wang, Xiao
Published: (2026)
Optimizing Length Compression in Large Reasoning Models
by: Cheng, Zhengxiang, et al.
Published: (2025)
by: Cheng, Zhengxiang, et al.
Published: (2025)
Reasoning Capabilities of Large Language Models on Dynamic Tasks
by: Wong, Annie, et al.
Published: (2025)
by: Wong, Annie, et al.
Published: (2025)
Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages
by: Tamang, S., et al.
Published: (2024)
by: Tamang, S., et al.
Published: (2024)
InftyThink: Breaking the Length Limits of Long-Context Reasoning in Large Language Models
by: Yan, Yuchen, et al.
Published: (2025)
by: Yan, Yuchen, et al.
Published: (2025)
Impact of Task Phrasing on Presumptions in Large Language Models
by: Ong, Kenneth J. K.
Published: (2026)
by: Ong, Kenneth J. K.
Published: (2026)
A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners
by: Jiang, Bowen, et al.
Published: (2024)
by: Jiang, Bowen, et al.
Published: (2024)
Eliciting Causal Abilities in Large Language Models for Reasoning Tasks
by: Wang, Yajing, et al.
Published: (2024)
by: Wang, Yajing, et al.
Published: (2024)
Simulated Reasoning is Reasoning
by: Kempt, Hendrik, et al.
Published: (2026)
by: Kempt, Hendrik, et al.
Published: (2026)
CROP: Token-Efficient Reasoning in Large Language Models via Regularized Prompt Optimization
by: Shah, Deep, et al.
Published: (2026)
by: Shah, Deep, et al.
Published: (2026)
Fundamental Limitations of Alignment in Large Language Models
by: Wolf, Yotam, et al.
Published: (2023)
by: Wolf, Yotam, et al.
Published: (2023)
More of the Same: Persistent Representational Harms Under Increased Representation
by: Mickel, Jennifer, et al.
Published: (2025)
by: Mickel, Jennifer, et al.
Published: (2025)
Reasoning Models Hallucinate More: Factuality-Aware Reinforcement Learning for Large Reasoning Models
by: Li, Junyi, et al.
Published: (2025)
by: Li, Junyi, et al.
Published: (2025)
Enhancing Large Language Model Performance To Answer Questions and Extract Information More Accurately
by: Zhang, Liang, et al.
Published: (2024)
by: Zhang, Liang, et al.
Published: (2024)
How do Scaling Laws Apply to Knowledge Graph Engineering Tasks? The Impact of Model Size on Large Language Model Performance
by: Heim, Desiree, et al.
Published: (2025)
by: Heim, Desiree, et al.
Published: (2025)
Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?
by: Greatrix, Thomas, et al.
Published: (2024)
by: Greatrix, Thomas, et al.
Published: (2024)
Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models
by: Lewis, Martha, et al.
Published: (2024)
by: Lewis, Martha, et al.
Published: (2024)
Understanding Artificial Theory of Mind: Perturbed Tasks and Reasoning in Large Language Models
by: Nickel, Christian, et al.
Published: (2026)
by: Nickel, Christian, et al.
Published: (2026)
Input-Time Scaling: Adding Noise and Irrelevance into Less-Is-More Drastically Improves Reasoning Performance and Efficiency
by: Huang, Rapheal, et al.
Published: (2025)
by: Huang, Rapheal, et al.
Published: (2025)
Exploring the Performance of Large Language Models on Subjective Span Identification Tasks
by: Dmonte, Alphaeus, et al.
Published: (2026)
by: Dmonte, Alphaeus, et al.
Published: (2026)
Understanding Understanding: A Pragmatic Framework Motivated by Large Language Models
by: Leyton-Brown, Kevin, et al.
Published: (2024)
by: Leyton-Brown, Kevin, et al.
Published: (2024)
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
by: Wang, Dixuan, et al.
Published: (2024)
by: Wang, Dixuan, et al.
Published: (2024)
Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost
by: Nayab, Sania, et al.
Published: (2024)
by: Nayab, Sania, et al.
Published: (2024)
Training Large Language Models To Reason In Parallel With Global Forking Tokens
by: Jia, Sheng, et al.
Published: (2025)
by: Jia, Sheng, et al.
Published: (2025)
Task-Centric Acceleration of Small-Language Models
by: Tsur, Dor, et al.
Published: (2026)
by: Tsur, Dor, et al.
Published: (2026)
Noiser: Bounded Input Perturbations for Attributing Large Language Models
by: Madani, Mohammad Reza Ghasemi, et al.
Published: (2025)
by: Madani, Mohammad Reza Ghasemi, et al.
Published: (2025)
Similar Items
-
State over Tokens: Characterizing the Role of Reasoning Tokens
by: Levy, Mosh, et al.
Published: (2025) -
Humans Perceive Wrong Narratives from AI Reasoning Texts
by: Levy, Mosh, et al.
Published: (2025) -
Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature
by: Katz, Uri, et al.
Published: (2024) -
Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds
by: Basmov, Victoria, et al.
Published: (2023) -
The Impact of Reasoning Step Length on Large Language Models
by: Jin, Mingyu, et al.
Published: (2024)