Idola Tribus of AI: Large Language Models tend to perceive order where none exists (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a tendency of large language models to generate absurd patterns is observed . authors say this is a limitation of the models' ability to perform complex tasks . |
| Approach: | We present a tendency of large language models to generate absurd patterns . authors conducted an experiment to evaluate logical consistency and self-coherence of LLMs . |
| Outcome: | a recent study shows that large language models generate absurd patterns despite their inadequacy . the model over-recognized patterns that were inconsistent with the given numbers, the study finds . |
Similar Papers
The Lawyer That Never Thinks: Consistency and Fairness as Keys to Reliable AI (2025.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used in high-stakes domains like law and research. |
| Approach: | They evaluate six leading Large Language Models on rationality, stability, and ethical fairness through reasoning tests, legal challenges, and bias-sensitive scenarios. |
| Outcome: | The models perform well on reasoning tests, legal challenges, and bias-sensitive scenarios. |
Beyond Memorization: Testing LLM Reasoning on Unseen Theory of Computation Tasks (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated strong performance on formal language tasks, yet whether this reflects genuine symbolic reasoning or pattern matching on familiar constructions remains unclear. |
| Approach: | They propose a benchmark for deterministic finite automata (DFA) construction from regular languages, comprising factual knowledge questions, seen construction problems from public sources, and unseen problems. |
| Outcome: | The proposed model achieves perfect accuracy on factual questions and 84-90% on seen tasks, but falls sharply on unseen problems (by 30-64%), with failures stemming from systematic misinterpretation of language constraints, incorrect handling of Kleene-star semantics, and a failure to preserve global consistency. |
Do Large Language Models Truly Grasp Addition? A Rule-Focused Diagnostic Using Two-Integer Arithmetic (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) excel at complex math but fail on basic addition, raising the question of whether they grasp rules or are merely reproducing patterns. |
| Approach: | They systematically probe LLMs’ understanding of two-integer addition by testing three crucial properties: commutativity (A+B=B+A), representation invariance via symbolic remapping and consistent accuracy scaling with operand length. |
| Outcome: | The proposed models achieve high numeric accuracy but fail basic addition tasks. |
Knowing the Facts but Choosing the Shortcut: Understanding How Large Language Models Compare Entities (2026.eacl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly used for knowledge-based reasoning tasks, yet understanding when they rely on genuine knowledge versus superficial heuristics remains challenging. |
| Approach: | They propose to ask LLMs to compare numerical attributes to find out which country has the highest population, France or Germany. |
| Outcome: | The proposed model comparisons show that heuristics override principled reasoning for larger models, while smaller models show no discrimination. |
Are Large Language Model Temporally Grounded? (2024.naacl-long)
Copied to clipboard
| Challenge: | Recent large language models lack a consistent temporal model of textual narratives . sentence ordering in unlabelled texts is only weakly correlated with event ordering . |
| Approach: | They evaluate LLMs with textual narratives and evaluate their common-sense knowledge . they find that LLM models struggle the most with self-consistency . |
| Outcome: | The proposed models lack a consistent temporal model of textual narratives. |
Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks (2024.acl-short)
Copied to clipboard
| Challenge: | Despite the generality and far-reaching consequences of large language models, there are still significant limitations making it difficult to apply them to certain tasks. |
| Approach: | They show that large language models can perform arithmetic tasks more robustly when conditioned on all of the correct higher-order digits. |
| Outcome: | The proposed model can predict the first digit of n-digit by m-digit multiplication without chain of thought reasoning, but in practice it fails to correctly predict the last digit on n digit by 1-digit multiplikation . |
Please note that I’m just an AI: Analysis of Behavior Patterns of LLMs in (Non-)offensive Speech Identification (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are becoming a part of our everyday lives by being used as tools for information search, content creation, writing assistance, and many more. |
| Approach: | They propose to use Large Language Models to detect offensive online language in applications with social risk, such as late-life companions and online content moderators. |
| Outcome: | The proposed models fail to detect offensive language and are therefore unsuitable for use in social applications such as late-life companions and online content moderators. |
Over-Reasoning and Redundant Calculation of Large Language Models (2024.eacl-short)
Copied to clipboard
| Challenge: | Large language models (LLMs) can solve problems step-by-step, but it is unclear whether they know when to use CoT and whether they are always necessary. |
| Approach: | They propose to use LLMs to generate redundant calculations and reasoning on a manually constructed math QA dataset, GSM8K-Zero. |
| Outcome: | The proposed model generates redundant calculations and reasoning on a manually constructed math QA dataset, but it is unclear whether it is necessary to use CoT reasoning. |
Can Large Language Models Always Solve Easy Problems if They Can Solve Harder Ones? (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive capabilities, but still suffer from inconsistency issues. |
| Approach: | They develop a ConsisEval benchmark to evaluate LLMs' inconsistency . they find that LLM models can paradoxically fail at easier problems . |
| Outcome: | The proposed model achieves highest consistency score but inconsistent to specific questions due to distraction by redundant information, misinterpretation of questions, etc. |
Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization (2023.findings-emnlp)
Copied to clipboard
| Challenge: | ChatGPT and GPT-4 are popular as evaluation metric for complex generative tasks . however, they are not ready as human replacements due to significant limitations . |
| Approach: | They conduct extensive analysis to examine the stability and reliability of LLMs as automatic evaluators for abstractive summarization. |
| Outcome: | The proposed methods outperform the commonly used automatic metrics but are not ready for human evaluation due to significant limitations. |