Challenge: Psychologists have documented several ways in which humans’ inferences deviate from the rules of logic.
Approach: They focus on syllogisms, which are inferences from two simple premises, and show that larger models are more logical than smaller ones.
Outcome: The results show that language models often mimic human biases, but overcome them in some cases.

Similar Papers

Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Dataset (2024.findings-acl)

Copied to clipboard

Challenge: Existing models of large language reasoning exhibit reasoning biases similar to humans, a study shows . syllogistic reasoning is one of the basic forms of deductive reasoning .
Approach: They propose to use a syllogism dataset to evaluate models' reasoning abilities . they propose to ask LLMs to translate slogismatic slurs into abstract logical expressions .
Outcome: The proposed method shows that models exhibit reasoning biases similar to humans, and that there is room for improvement in reasoning problems where premises and hypotheses are neither entailment nor contradiction.
A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences (2024.emnlp-main)

Copied to clipboard

Challenge: syllogistic reasoning is a deductive reasoning skill that is crucial in everyday problem-solving and decision-making experiences.
Approach: They propose to study the reasoning abilities of Large Language Models (LLMs) they propose to use supervised fine-tuning and chain-of-thought reasoning to investigate their results.
Outcome: The proposed models exhibit reasoning biases, avoid answering that no conclusion follows, align with human difficulties, and struggle with multi-step reasoning.
Logical forms complement probability in understanding language model (and human) performance (2025.acl-long)

Copied to clipboard

Challenge: Existing studies on LLMs have shown that they perform well on logical reasoning problems, but there is still a lack of fine-grained understanding of the logical forms.
Approach: They propose a dataset of hypothetical and disjunctive syllogisms in propositional and modal logic and use it as the testbed for understanding LLM performance.
Outcome: The proposed model performs well on proposi-tional and modal logics, but does it exhibit preferences for certain argument forms?
Systematic Biases in LLM Simulations of Debates (2024.emnlp-main)

Copied to clipboard

Challenge: Current research suggests that LLM-based agents become increasingly human-like in their performance, sparking interest in using these AI agents as substitutes for human participants in behavioral studies.
Approach: They propose to use LLMs to simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes.
Outcome: The proposed model can simulate political debates on topics that are important aspects of people’s day-to-day lives and decision-making processes.
A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners (2024.emnlp-main)

Copied to clipboard

Challenge: a new hypothesis-testing framework is developed to assess whether large language models possess genuine reasoning abilities or primarily depend on token bias.
Approach: They propose a framework to assess whether large language models have genuine reasoning abilities or primarily depend on token bias.
Outcome: The proposed framework outlines a list of hypotheses where token biases are readily identifiable . the results suggest that most LLMs still struggle with logical reasoning .
Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference (2025.findings-acl)

Copied to clipboard

Challenge: Recent studies on reasoning in language models have sparked a debate on whether they can learn systematic inferential principles or merely exploit superficial patterns in the training data.
Approach: They propose a method for circuit discovery aimed at interpreting syllogistic inference . they uncover a circuit involving middle-term suppression that elucidates how LMs transfer information to derive valid conclusions from premises.
Outcome: The proposed method elucidates how LMs transfer information to derive valid conclusions from premises.
Persona-Assigned Large Language Models Exhibit Human-Like Motivated Reasoning (2026.findings-acl)

Copied to clipboard

Challenge: Prior studies have reported that large language models (LLMs) are also susceptible to human-like cognitive biases, but the extent to which LLMs selectively reason toward identity-congruent conclusions remains unexplored.
Approach: They investigate whether assigning 8 personas across 4 political and socio-demographic attributes induces motivated reasoning in LLMs.
Outcome: The proposed model is assigned 8 personas across 4 political and socio-demographic attributes and shows that they have 9% reduced veracity discernment compared to models without persona.
Comparing Inferential Strategies of Humans and Large Language Models in Deductive Reasoning (2024.acl-long)

Copied to clipboard

Challenge: Recent advances in the domain of large language models (LLMs) have showcased their capability in executing deductive reasoning tasks.
Approach: They examine inferential strategies employed by large language models through a detailed evaluation of their responses to propositional logic problems.
Outcome: The proposed model shows that it displays reasoning patterns similar to humans, including strategies like supposition following or chain construction.
Can Activation Steering Generalize Across Languages? A Study on Syllogistic Reasoning in Language Models (2026.eacl-long)

Copied to clipboard

Challenge: Prior work has focused on activation steering for Large Language Models (LLMs) this technique can be used to improve reasoning accuracy and transferability across languages.
Approach: They propose to use activation steering to steer models towards a cross-lingual reasoning space.
Outcome: The proposed techniques generalise well to multilingual datasets while minimizing language modelling performance.
How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects (2026.findings-acl)

Copied to clipboard

Challenge: a number of theories have been proposed to account for content effects in large language models, including the dual-process theory of reasoning, but the mechanisms behind content effects remain unclear.
Approach: They propose to encode validity and plausibility concepts in LLMs by aligning them in representational geometry.
Outcome: The proposed model conflates validity and plausibility, and vice versa.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations