Why are LLMs' abilities emergent?
Fuente:
arXiv
Saved in:
| Main Author: | Havlík, Vladimír |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Large Linguistic Models: Investigating LLMs' metalinguistic abilities
by: Beguš, Gašper, et al.
Published: (2023)
by: Beguš, Gašper, et al.
Published: (2023)
Factors affecting the in-context learning abilities of LLMs for dialogue state tracking
by: Hegde, Pradyoth, et al.
Published: (2025)
by: Hegde, Pradyoth, et al.
Published: (2025)
Surgical Feature-Space Decomposition of LLMs: Why, When and How?
by: Chavan, Arnav, et al.
Published: (2024)
by: Chavan, Arnav, et al.
Published: (2024)
Look Within, Why LLMs Hallucinate: A Causal Perspective
by: Li, He, et al.
Published: (2024)
by: Li, He, et al.
Published: (2024)
Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
by: Roy, Amartya, et al.
Published: (2026)
by: Roy, Amartya, et al.
Published: (2026)
Why Does New Knowledge Create Messy Ripple Effects in LLMs?
by: Qin, Jiaxin, et al.
Published: (2024)
by: Qin, Jiaxin, et al.
Published: (2024)
Why are all LLMs Obsessed with Japanese Culture? On the Hidden Cultural and Regional Biases of LLMs
by: de Landa, Joseba Fernandez, et al.
Published: (2026)
by: de Landa, Joseba Fernandez, et al.
Published: (2026)
Can AI mimic the human ability to define neologisms?
by: Georgiou, Georgios P.
Published: (2025)
by: Georgiou, Georgios P.
Published: (2025)
Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations
by: Li, Shanghao, et al.
Published: (2026)
by: Li, Shanghao, et al.
Published: (2026)
LongTail-Swap: benchmarking language models' abilities on rare words
by: Algayres, Robin, et al.
Published: (2025)
by: Algayres, Robin, et al.
Published: (2025)
On the token distance modeling ability of higher RoPE attention dimension
by: Hong, Xiangyu, et al.
Published: (2024)
by: Hong, Xiangyu, et al.
Published: (2024)
Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs
by: Cheng, Myra, et al.
Published: (2026)
by: Cheng, Myra, et al.
Published: (2026)
Semantic Anchors in In-Context Learning: Why Small LLMs Cannot Flip Their Labels
by: Kumar, Anantha Padmanaban Krishna
Published: (2025)
by: Kumar, Anantha Padmanaban Krishna
Published: (2025)
Adaptation Odyssey in LLMs: Why Does Additional Pretraining Sometimes Fail to Improve?
by: Öncel, Fırat, et al.
Published: (2024)
by: Öncel, Fırat, et al.
Published: (2024)
Why We Need World Models for AGI: Where LLMs Fail and How World Models May Outperform
by: Alaswad, Feisal, et al.
Published: (2026)
by: Alaswad, Feisal, et al.
Published: (2026)
SLPL SHROOM at SemEval2024 Task 06: A comprehensive study on models ability to detect hallucination
by: Fallah, Pouya, et al.
Published: (2024)
by: Fallah, Pouya, et al.
Published: (2024)
Why Attend to Everything? Focus is the Key
by: Yao, Hengshuai, et al.
Published: (2026)
by: Yao, Hengshuai, et al.
Published: (2026)
Code Summarization Beyond Function Level
by: Makharev, Vladimir, et al.
Published: (2025)
by: Makharev, Vladimir, et al.
Published: (2025)
Why Slop Matters
by: Kommers, Cody, et al.
Published: (2025)
by: Kommers, Cody, et al.
Published: (2025)
Why Chain of Thought Fails in Clinical Text Understanding
by: Wu, Jiageng, et al.
Published: (2025)
by: Wu, Jiageng, et al.
Published: (2025)
Why Braking? Scenario Extraction and Reasoning Utilizing LLM
by: Wu, Yin, et al.
Published: (2025)
by: Wu, Yin, et al.
Published: (2025)
Left-right asymmetry in predicting brain activity from LLMs' representations emerges with their formal linguistic competence
by: Bonnasse-Gahot, Laurent, et al.
Published: (2026)
by: Bonnasse-Gahot, Laurent, et al.
Published: (2026)
Semantic Gravity Wells: Why Negative Constraints Backfire
by: Rana, Shailesh
Published: (2026)
by: Rana, Shailesh
Published: (2026)
Why is constrained neural language generation particularly challenging?
by: Garbacea, Cristina, et al.
Published: (2022)
by: Garbacea, Cristina, et al.
Published: (2022)
Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actions
by: Sobotka, Jan, et al.
Published: (2026)
by: Sobotka, Jan, et al.
Published: (2026)
Why Retrieval-Augmented Generation Fails: A Graph Perspective
by: Guo, Kai, et al.
Published: (2026)
by: Guo, Kai, et al.
Published: (2026)
The Stepwise Informativeness Assumption: Why are Entropy Dynamics and Reasoning Correlated in LLMs?
by: Català, Mar Gonzàlez I, et al.
Published: (2026)
by: Català, Mar Gonzàlez I, et al.
Published: (2026)
Attention Basin: Why Contextual Position Matters in Large Language Models
by: Yi, Zihao, et al.
Published: (2025)
by: Yi, Zihao, et al.
Published: (2025)
Why Did Apple Fall: Evaluating Curiosity in Large Language Models
by: Wang, Haoyu, et al.
Published: (2025)
by: Wang, Haoyu, et al.
Published: (2025)
From What to Why: Thought-Space Recommendation with Small Language Models
by: Biswas, Prosenjit, et al.
Published: (2025)
by: Biswas, Prosenjit, et al.
Published: (2025)
LLM Olympiad: Why Model Evaluation Needs a Sealed Exam
by: Cruz, Jan Christian Blaise, et al.
Published: (2026)
by: Cruz, Jan Christian Blaise, et al.
Published: (2026)
The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution
by: Qian, Chen, et al.
Published: (2026)
by: Qian, Chen, et al.
Published: (2026)
Revealing emergent human-like conceptual representations from language prediction
by: Xu, Ningyu, et al.
Published: (2025)
by: Xu, Ningyu, et al.
Published: (2025)
Tracing the ongoing emergence of human-like reasoning in Large Language Models
by: Morosi, Paolo, et al.
Published: (2026)
by: Morosi, Paolo, et al.
Published: (2026)
Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs
by: Bergsma, Shane, et al.
Published: (2025)
by: Bergsma, Shane, et al.
Published: (2025)
Valley: Video Assistant with Large Language model Enhanced abilitY
by: Luo, Ruipu, et al.
Published: (2023)
by: Luo, Ruipu, et al.
Published: (2023)
A Theoretical Analysis of Why Masked Diffusion Models Mitigate the Reversal Curse
by: Jeon, Moongyu, et al.
Published: (2026)
by: Jeon, Moongyu, et al.
Published: (2026)
Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
by: Li, Pengxiang, et al.
Published: (2026)
by: Li, Pengxiang, et al.
Published: (2026)
CrisiText: A dataset of warning messages for LLM training in emergency communication
by: Gonella, Giacomo, et al.
Published: (2025)
by: Gonella, Giacomo, et al.
Published: (2025)
Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs
by: Tie, Guiyao, et al.
Published: (2025)
by: Tie, Guiyao, et al.
Published: (2025)
Similar Items
-
Large Linguistic Models: Investigating LLMs' metalinguistic abilities
by: Beguš, Gašper, et al.
Published: (2023) -
Factors affecting the in-context learning abilities of LLMs for dialogue state tracking
by: Hegde, Pradyoth, et al.
Published: (2025) -
Surgical Feature-Space Decomposition of LLMs: Why, When and How?
by: Chavan, Arnav, et al.
Published: (2024) -
Look Within, Why LLMs Hallucinate: A Causal Perspective
by: Li, He, et al.
Published: (2024) -
Why LLMs Fail at Causal Discovery and How Interventional Agents Escape
by: Roy, Amartya, et al.
Published: (2026)