Large Language Models are Zero-Shot Reasoners

2022/05/24 by Takeshi Kojima, Kojima, Takeshi, Shixiang Gu +8 · 10 voices · 713 citations
Computer Science · #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling #cs.AI #cs.CL #cs.LG

paper · pdf · doi:10.48550/arxiv.2205.11916

openalex publication_date 2022/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Pretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique for eliciting complex multi-step reasoning through step-by-step answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficult system-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability for few-shot learning, we show that LLMs are decent zero-shot reasoners by simply adding "Let's think step by step" before each answer. Experimental results demonstrate that our Zero-shot-CoT, using the same single prompt template, significantly outperforms zero-shot LLM performances on diverse benchmark reasoning tasks including arithmetics (MultiArith, GSM8K, AQUA-RAT, SVAMP), symbolic reasoning (Last Letter, Coin Flip), and other logical reasoning tasks (Date Understanding, Tracking Shuffled Objects), without any hand-crafted few-shot examples, e.g. increasing the accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with large InstructGPT model (text-davinci-002), as well as similar magnitudes of improvements with another off-the-shelf large model, 540B parameter PaLM. The versatility of this single prompt across very diverse reasoning tasks hints at untapped and understudied fundamental zero-shot capabilities of LLMs, suggesting high-level, multi-task broad cognitive capabilities may be extracted by simple prompting. We hope our work not only serves as the minimal strongest zero-shot baseline for the challenging reasoning benchmarks, but also highlights the importance of carefully exploring and analyzing the enormous zero-shot knowledge hidden inside LLMs before crafting finetuning datasets or few-shot exemplars.

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• Can Speech LLMs Think while Listening?
• MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference
• TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
• EconCausal: A Context-Aware Economic Reasoning Benchmark for Large Language Models
• Reasoning for Hierarchical Text Classification: The Case of Patents
• Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts
• VelLMes: A high-interaction AI-based deception framework
• Revisiting the Uniform Information Density Hypothesis in LLM Reasoning Traces
• SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
• Unlocking Latent Discourse Translation in LLMs Through Quality-Aware Decoding
• Towards Interpretable and Inference-Optimal COT Reasoning with Sparse Autoencoder-Guided Generation
• Do LLMs Know They Are Being Tested? Evaluation Awareness and Incentive-Sensitive Failures in GPT-OSS-20B
• GPT-5 Model Corrected GPT-4V's Chart Reading Errors, Not Prompting
• Adaptive LLM-Symbolic Reasoning via Dynamic Logical Solver Composition
• From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond
• Get RICH or Die Scaling: Profitably Trading Inference Compute for Robustness
• Self-signals Driven Multi-LLM Debate for Efficient and Accurate Reasoning
• Valid Stopping for LLM Generation via Empirical Dynamic Formal Lift
• LexiCon: a Benchmark for Planning under Temporal Constraints in Natural Language
• ARM: Discovering Agentic Reasoning Modules for Generalizable Multi-Agent Systems
• GraphGhost: Tracing Structures Behind Large Language Models
• When Thinking Drifts: Evidential Grounding for Robust Video Reasoning
• Code-Switching In-Context Learning for Cross-Lingual Transfer of Large Language Models
• AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering
• SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
• Resource-Efficient Fine-Tuning of LLaMA-3.2-3B for Medical Chain-of-Thought Reasoning
• Natural Language Edge Labelling: Decoupling Intent from Execution in Structured LM Reasoning
• MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts
• Self-Filtered Distillation with LLMs-generated Trust Indicators for Reliable Patent Classification
• Thinking on the Fly: Test-Time Reasoning Enhancement via Latent Thought Policy Optimization
• FaithCoT-Bench: Benchmarking Instance-Level Faithfulness of Chain-of-Thought Reasoning
• Evaluation of Clinical Trials Reporting Quality using Large Language Models
• QuiLL: An LLM-Based Vulnerability Assessment Framework for the Wild
• From Filters to VLMs: Benchmarking Defogging Methods through Object Detection and Segmentation Performance
• Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models
• Generating High-Level Test Cases from Requirements using LLM: An Industry Study
• MITS: Enhanced Tree Search Reasoning for LLMs via Pointwise Mutual Information
• Backdoor-Powered Prompt Injection Attacks Nullify Defense Methods
• GuidedSampling: Steering LLMs Towards Diverse Candidate Solutions at Inference-Time
• Lateral Tree-of-Thoughts Surpasses ToT by Incorporating Logically-Consistent, Low-Utility Candidates
• Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents
• CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning
• NCV: A Node-Wise Consistency Verification Approach for Low-Cost Structured Error Localization in LLM Reasoning
• Knowledge Graph-Guided Multi-Agent Distillation for Reliable Industrial Question Answering with Datasets
• SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models
• StepChain GraphRAG: Reasoning Over Knowledge Graphs for Multi-Hop Question Answering
• Fine-Tuning on Noisy Instructions: Effects on Generalization and Performance
• GRAD: Generative Retrieval-Aligned Demonstration Sampler for Efficient Few-Shot Reasoning
• PromptPilot: Improving Human-AI Collaboration Through LLM-Enhanced Prompt Engineering
• Rethinking Reward Models for Multi-Domain Test-Time Scaling
• Enhancing Rating Prediction with Off-the-Shelf LLMs Using In-Context User Reviews
• Facilitating Cognitive Accessibility with LLMs: A Multi-Task Approach to Easy-to-Read Text Generation
• GRPO-λ: Credit Assignment improves LLM Reasoning
• TAMA: Tool-Augmented Multimodal Agent for Procedural Activity Understanding
• Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search
• Latent Thinking Optimization: Your Latent Reasoning Language Model Secretly Encodes Reward Signals in Its Latent Thoughts
• Beyond Linear Probes: Dynamic Safety Monitoring for Language Models
• More Thought, Less Accuracy? On the Dual Nature of Reasoning in Vision-Language Models
• RoBiologyDataChoiceQA: A Romanian Dataset for improving Biology understanding of Large Language Models
• SING-SQL: A Synthetic Data Generation Framework for In-Domain Text-to-SQL Translation
• Nudging the Boundaries of LLM Reasoning
• Unspoken Hints: Accuracy Without Acknowledgement in LLM Reasoning
• Hierarchical Reasoning Models: Perspectives and Misconceptions
• Planner-R1: Reward Shaping Enables Efficient Agentic RL with Smaller LLMs
• Plug-and-Play Emotion Graphs for Compositional Prompting in Zero-Shot Speech Emotion Recognition
• From Perception to Cognition: A Survey of Vision-Language Interactive Reasoning in Multimodal Large Language Models
• Agentic Exploration of Physics Models
• The Dialogue That Heals: A Comprehensive Evaluation of Doctor Agents' Inquiry Capability
• MobileLLM-R1: Exploring the Limits of Sub-Billion Language Model Reasoners with Open Training Recipes
• TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models
• Learning to Ponder: Adaptive Reasoning in Latent Space
• Advancing mathematics research with generative AI
• Think Twice, Generate Once: Safeguarding by Progressive Self-Reflection
• Act as an expert in psychometry. The evaluation of large language models utility in psychological tests cross-cultural adaptations
• ContextPRM: Leveraging Contextual Coherence for multi-domain Test-Time Scaling
• Rethinking and Benchmarking Large Language Models for Graph Reasoning
• ReasonCACHE: Teaching LLMs To Reason Without Weight Updates
• VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes
• Beyond Magic Words: Sharpness-Aware Prompt Evolving for Robust Large Language Models with TARE
• ByteSized32Refactored: Towards an Extensible Interactive Text Games Corpus for LLM World Modeling and Evaluation
• Bridging the Knowledge-Prediction Gap in LLMs on Multiple-Choice Questions
• Fast Thinking for Large Language Models
• Towards Efficient CoT Distillation: Self-Guided Rationale Selector for Better Performance with Fewer Rationales
• An Senegalese Legal Texts Structuration Using LLM-augmented Knowledge Graph
• PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR
• No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimization
• MedCritical: Enhancing Medical Reasoning in Small Language Models via Self-Collaborative Correction
• Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces
• Tracing Uncertainty in Language Model "Reasoning"
• BuildBench: Benchmarking LLM Agents on Compiling Real-World Open-Source Software
• UML-CoT: Structured Reasoning and Planning with Unified Modeling Language for Robotic Room Cleaning
• Investigating Faithfulness in Large Audio Language Models
• Green Prompt Engineering: Investigating the Energy Impact of Prompt Design in Software Engineering
• When Does Reasoning Matter? A Controlled Study of Reasoning's Contribution to Model Performance
• From Long to Lean: Performance-aware and Adaptive Chain-of-Thought Compression via Multi-round Refinement
• Fuzzy Reasoning Chain (FRC): An Innovative Reasoning Framework from Fuzziness to Clarity
• No Prompt Left Behind: Exploiting Zero-Variance Prompts in LLM Reinforcement Learning via Entropy-Guided Advantage Shaping
• Reimagining Agent-based Modeling with Large Language Model Agents via Shachi
• Mixture-of-Visual-Thoughts: Exploring Context-Adaptive Reasoning Mode Selection for General Visual Reasoning
• Thinking with Sound: Audio Chain-of-Thought Enables Multimodal Reasoning in Large Audio-Language Models
• Why Chain of Thought Fails in Clinical Text Understanding
• Towards Transparent AI: A Survey on Explainable Language Models
• Correct Reasoning Paths Visit Shared Decision Pivots
• Plan2Evolve: LLM Self-Evolution for Improved Planning Capability via Automated Domain Generation
• Dual-Head Reasoning Distillation: Improving Classifier Accuracy with Train-Time-Only Reasoning
• Talking Trees: Reasoning-Assisted Induction of Decision Trees for Tabular Data
• Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond
• Bidirectional Intention Inference Enhances LLMs' Defense Against Multi-Turn Jailbreak Attacks
• Best-of-∞ -- Asymptotic Performance of Test-Time Compute
• UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition
• Fine-tuning of Large Language Models for Domain-Specific Cybersecurity Knowledge
• Leveraging What's Overfixed: Post-Correction via LLM Grammatical Error Overcorrection
• A Fano-Style Accuracy Upper Bound for LLM Single-Pass Reasoning in Multi-Hop QA
• CLAUSE: Agentic Neuro-Symbolic Knowledge Graph Reasoning via Dynamic Learnable Context Engineering
• Parallel Thinking, Sequential Answering: Bridging NAR and AR for Efficient Reasoning
• Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation
• Large Language Models for Real-World IoT Device Identification
• MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
• Thinking While Listening: Simple Test Time Scaling For Audio Classification
• Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation
• LOCA: Logical Chain Augmentation for Scientific Corpus Cleaning
• Scaling LLM-Driven Multi-Agent Systems: Design Principles and Architectural Scalability Analysis
• Distilling Answer Set Programming Theories from Large Language Models
• Hierarchical Latent Reasoning for LLM-based Recommendation
• Would You Walk to the Car Wash? Revealing the Salience Bias of Large Language Models in Commonsense Reasoning
• LASAR: Latent Adaptive Semantic Aligned Reasoning for Generative Recommendation
• Can Large Language Models Resolve Semantic Discrepancy in Self-Destructive Subcultures? Evidence from Jirai Kei
• A Theory of Appropriateness That Accounts for Norms of Rationality
• Language Models Coupled with Metacognition Can Outperform Reasoning Models
• LLMs as verification oracles for Solidity
• Citrus-V: Advancing Medical Foundation Models with Unified Medical Image Grounding for Clinical Reasoning
• Unveiling Chain of Step Reasoning for Vision-Language Models with Fine-grained Rewards
• LLM-Enhanced Self-Evolving Reinforcement Learning for Multi-Step E-Commerce Payment Fraud Risk Detection
• Solving Math Word Problems Using Estimation Verification and Equation Generation
• Live-E2T: Real-time Threat Monitoring in Video via Deduplicated Event Reasoning and Chain-of-Thought
• Autonomous Data Agents: A New Opportunity for Smart Data
• Evaluating Large Language Models for Detecting Antisemitism
• Through the Lens of Human-Human Collaboration: A Configurable Research Platform for Exploring Human-Agent Collaboration
• WISE: Weak-Supervision-Guided Step-by-Step Explanations for Multimodal LLMs in Image Classification
• Correlation or Causation: Analyzing the Causal Structures of LLM and LRM Reasoning Process
• Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling
• Program Synthesis via Test-Time Transduction
• Mitigating Strategy-Selection Bias in Reasoning for More Effective Test-Time Scaling
• Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
• Automated Facility Enumeration for Building Compliance Checking using Door Detection and Large Language Models
• Adaptive Overclocking: Dynamic Control of Thinking Path Length via Real-Time Reasoning Signals
• Uncovering Implicit Bias in Large Language Models with Concept Learning Dataset
• seqBench: A Tunable Benchmark to Quantify Sequential Reasoning Limits of LLMs
• KAHAN: Knowledge-Augmented Hierarchical Analysis and Narration for Financial Data Narration
• MoEs Are Stronger than You Think: Hyper-Parallel Inference Scaling with RoE
• Cognitive Linguistic Identity Fusion Score (CLIFS): A Scalable Cognition-Informed Approach to Quantifying Identity Fusion from Text
• A Novel Differential Feature Learning for Effective Hallucination Detection and Classification
• Challenging the Evaluator: LLM Sycophancy Under User Rebuttal
• Enhancing Scientific Visual Question Answering via Vision-Caption aware Supervised Fine-Tuning
• Towards Universal Debiasing for Language Models-based Tabular Data Generation
• RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
• Best-of-L: Cross-Lingual Reward Modeling for Mathematical Reasoning
• TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies?
• Can LLMs Judge Debates? Evaluating Non-Linear Reasoning via Argumentation Theory Semantics
• DivLogicEval: A Framework for Benchmarking Logical Reasoning Evaluation in Large Language Models
• Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning
• MIRA: Empowering One-Touch AI Services on Smartphones with MLLM-based Instruction Recommendation
• Prompt Stability in Code LLMs: Measuring Sensitivity across Emotion- and Personality-Driven Variations
• Early Stopping Chain-of-thoughts in Large Language Models
• Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
• Improving Context Fidelity via Native Retrieval-Augmented Reasoning
• Explain Before You Answer: A Survey on Compositional Visual Reasoning
• REAMS: Reasoning Enhanced Algorithm for Maths Solving
• Evaluating LLM Alignment on Personality Inference from Real-World Interview Data
• The Few-shot Dilemma: Over-prompting Large Language Models
• FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health
• Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models
• Don't Change My View: Ideological Bias Auditing in Large Language Models
• Empowering LLMs with Parameterized Skills for Adversarial Long-Horizon Planning
• Root Cause Analysis of Radiation Oncology Incidents Using Large Language Models
• Evaluating Large Language Models for Functional and Maintainable Code in Industrial Settings: A Case Study at ASML
• Pun Unintended: LLMs and the Illusion of Humor Understanding
• GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models
• POT: Inducing Overthinking in LLMs via Black-Box Iterative Optimization
• Formal Reasoning for Intelligent QA Systems: A Case Study in the Educational Domain
• LVLMs are Bad at Overhearing Human Referential Communication
• Rethinking Technology Stack Selection with AI Coding Proficiency
• Teaching LLMs to Plan: Logical Chain-of-Thought Instruction Tuning for Symbolic Planning
• The System Description of CPS Team for Track on Driving with Language of CVPR 2024 Autonomous Grand Challenge
• LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences
• Reasoning Under Uncertainty: Exploring Probabilistic Reasoning Capabilities of LLMs
• Unveiling the Latent Directions of Reflection in Large Language Models
• VARCO-VISION-2.0 Technical Report
• Generating Energy-Efficient Code via Large-Language Models -- Where are we now?
• Unsupervised Hallucination Detection by Inspecting Reasoning Processes
• Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning
• HieroAction: Hierarchically Guided VLM for Fine-Grained Action Analysis
• XAgents: A Unified Framework for Multi-Agent Cooperation via IF-THEN Rules and Multipolar Task Processing Graph
• Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
• Fluent but Unfeeling: The Emotional Blind Spots of Language Models
• Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
• TextOnly: A Unified Function Portal for Text-Related Functions on Smartphones
• AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives
• Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals
• Learning from Diverse Reasoning Paths with Routing and Collaboration
• Ensemble Distribution Distillation for Self-Supervised Human Activity Recognition
• SPADE: A Large Language Model Framework for Soil Moisture Pattern Recognition and Anomaly Detection in Precision Agriculture
• Bias after Prompting: Persistent Discrimination in Large Language Models
• Are LLMs Enough for Hyperpartisan, Fake, Polarized and Harmful Content Detection? Evaluating In-Context Learning vs. Fine-Tuning
• PersonaFuse: A Personality Activation-Driven Framework for Enhancing Human-LLM Interactions
• What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
• How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models
• Video Parallel Scaling: Aggregating Diverse Frame Subsets for VideoLLMs
• Paladin: Defending LLM-enabled Phishing Emails with a New Trigger-Tag Paradigm
• RAFFLES: Reasoning-based Attribution of Faults for LLM Systems
• Can AI Make Energy Retrofit Decisions? An Evaluation of Large Language Models
• From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs
• On the Same Wavelength? Evaluating Pragmatic Reasoning in Language Models across Broad Concepts
• Empirical Study of Code Large Language Models for Binary Security Patch Detection
• From Long to Short: LLMs Excel at Trimming Own Reasoning Chains
• DRF: LLM-AGENT Dynamic Reputation Filtering Framework
• Mitigating Spurious Correlations Between Question and Answer via Chain-of-Thought Correctness Perception Distillation
• Less is More Tokens: Efficient Math Reasoning via Difficulty-Aware Chain-of-Thought Distillation
• DreamPRM-1.5: Unlocking the Potential of Each Instance for Multimodal Process Reward Model Training
• ThumbnailTruth: A Multi-Modal LLM Approach for Detecting Misleading YouTube Thumbnails Across Diverse Cultural Settings
• On Robustness and Reliability of Benchmark-Based Evaluation of LLMs
• Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought
• A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models
• Are LLM Agents Behaviorally Coherent? Latent Profiles for Social Simulation
• ProMQA-Assembly: Multimodal Procedural QA Dataset on Assembly
• Are We SOLID Yet? An Empirical Study on Prompting LLMs to Detect Design Principle Violations
• DRAssist: Dispute Resolution Assistance using Large Language Models
• HF-RAG: Hierarchical Fusion-based RAG with Multiple Sources and Rankers
• Baichuan-M2: Scaling Medical Capability with Large Verifier System
• Inducing Faithfulness in Structured Reasoning via Counterfactual Sensitivity
• Reasoning Vectors: Transferring Chain-of-Thought Capabilities via Task Arithmetic
• Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors
• MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs
• Vis-CoT: A Human-in-the-Loop Framework for Interactive Visualization and Intervention in LLM Chain-of-Thought Reasoning
• LLM-HyPZ: Hardware Vulnerability Discovery using an LLM-Assisted Hybrid Platform for Zero-Shot Knowledge Extraction and Refinement
• RPRO: Ranked Preference Reinforcement Optimization for Enhancing Medical QA and Diagnostic Reasoning
• Can Multi-turn Self-refined Single Agent LMs with Retrieval Solve Hard Coding Problems?
• Memory Limitations of Prompt Tuning in Transformers
• Extraction of biological terms using large language models enhances the usability of metadata in the BioSample database
• Explainable Chain-of-Thought Reasoning: An Empirical Analysis on State-Aware Reasoning Dynamics
• PiCSAR: Probabilistic Confidence Selection And Ranking for Reasoning Chains
• How Well Do Vision--Language Models Understand Cities? A Comparative Study on Spatial Reasoning from Street-View Images
• From Canonical to Complex: Benchmarking LLM Capabilities in Undergraduate Thermodynamics
• A Survey of Reasoning with Foundation Models: Concepts, Methodologies, and Outlook
• Re4: Scientific Computing Agent with Rewriting, Resolution, Review and Revision
• MindGuard: Intrinsic Decision Inspection for Securing LLM Agents Against Metadata Poisoning
• Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning
• Automated Quality Assessment for LLM-Based Complex Qualitative Coding: A Confidence-Diversity Framework
• AI reasoning effort predicts human decision time in content moderation
• AI for Statutory Simplification: A Comprehensive State Legal Corpus and Labor Benchmark
• Demystifying Scientific Problem-Solving in LLMs by Probing Knowledge and Reasoning
• Do LVLMs Know What They Know? A Systematic Study of Knowledge Boundary Perception in LVLMs
• Reasoning LLMs in the Medical Domain: A Literature Survey
• Beyond the Textual: Generating Coherent Visual Options for MCQs
• Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units
• Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval
• Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
• STRATA-TS: Selective Knowledge Transfer for Urban Time Series Forecasting with Retrieval-Guided Reasoning
• TrackRec: Iterative Alternating Feedback with Chain-of-Thought via Preference Alignment for Recommendation
• Adaptive Originality Filtering: Rejection Based Prompting and RiddleScore for Culturally Grounded Multilingual Riddle Generation
• Language Models For Generalised PDDL Planning: Synthesising Sound and Programmatic Policies
• SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models
• RETAIL: Towards Real-world Travel Planning for Large Language Models
• When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models
• Transduction is All You Need for Structured Data Workflows
• Trust but Verify! A Survey on Verification Design for Test-time Scaling
• The Prompting Brain: Neurocognitive Markers of Expertise in Guiding Large Language Models
• Embedding Democratic Values into Social Media AIs via Societal Objective Functions
• Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
• Exit Stories: Using Reddit Self-Disclosures to Understand Disengagement from Problematic Communities
• CausalPlan: Empowering Efficient LLM Multi-Agent Collaboration Through Causality-Driven Planning
• MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers
• G²RPO-A: Guided Group Relative Policy Optimization with Adaptive Guidance
• OptimalThinkingBench: Evaluating Over and Underthinking in LLMs
• Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction
• Is GPT-OSS Good? A Comprehensive Evaluation of OpenAI's Latest Open Source Models
• TaoSR1: The Thinking Model for E-commerce Relevance Search
• Synchronization Dynamics of Heterogeneous, Collaborative Multi-Agent AI Systems
• Fast, Slow, and Tool-augmented Thinking for LLMs: A Review
• Cognitive Decision Routing in Large Language Models: When to Think Fast, When to Think Slow
• Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications
• Mitigating Hallucinations in Large Language Models via Causal Reasoning
• RISE: Enhancing VLM Image Annotation with Self-Supervised Reasoning
• Exploring Efficiency Frontiers of Thinking Budget in Medical Reasoning: Scaling Laws between Computational Resources and Reasoning Quality
• Simple o3: Towards Interleaved Vision-Language Reasoning
• Overcoming Knowledge Discrepancies: Structuring Reasoning Threads through Knowledge Balancing in Interactive Scenarios
• Defects4Log: Benchmarking LLMs for Logging Code Defect Detection and Reasoning
• MM-R1: Unleashing the Power of Unified Multimodal Large Language Models for Personalized Image Generation
• HOID-R1: Reinforcement Learning for Open-World Human-Object Interaction Detection Reasoning with Multimodal Large Language Model
• MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents
• Ask ChatGPT: Caveats and Mitigations for Individual Users of AI Chatbots
• Benchmark-Driven Selection of AI: Evidence from DeepSeek-R1
• MEML-GRPO: Heterogeneous Multi-Expert Mutual Learning for RLVR Advancement
• CS-Agent: LLM-based Community Search via Dual-agent Collaboration
• LLM Empowered Prototype Learning for Zero and Few-Shot Tasks on Tabular Data
• CARES: Collaborative Agentic Reasoning for Error Detection in Surgery
• GreenTEA: Gradient Descent with Topic-modeling and Evolutionary Auto-prompting
• M²LLM: Multi-view Molecular Representation Learning with Large Language Models
• CoDAE: Adapting Large Language Models for Education via Chain-of-Thought Data Augmentation
• \(X\)-evolve: Solution space evolution powered by large language models
• Evaluating Large Language Models as Expert Annotators
• ThinkTuning: Instilling Cognitive Reflections without Distillation
• Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression
• CoT-Pose: Chain-of-Thought Reasoning for 3D Pose Generation from Abstract Prompts
• Keyword-Centric Prompting for One-Shot Event Detection with Self-Generated Rationale Enhancements
• Can You Trick the Grader? Adversarial Persuasion of LLM Judges
• Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
• Confidence Estimation for Text-to-SQL in Large Language Models
• The benefits and dangers of anthropomorphic conversational agents
• The Problem of Atypicality in LLM-Powered Psychiatry
• Contrastive Analysis of Constituent Order Preferences Within Adverbial Roles in English and Chinese News: A Large-Language-Model-Driven Approach
• UR²: Unify RAG and Reasoning through Reinforcement Learning
• SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs
• PanelTR: Zero-Shot Table Reasoning Framework Through Multi-Agent Scientific Discussion
• Efficient Knowledge Probing of Large Language Models by Adapting Pre-trained Embeddings
• Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education
• Omni Geometry Representation Learning vs Large Language Models for Geospatial Entity Resolution
• Large Language Models for Oral History Understanding with Text Classification and Sentiment Analysis
• FineDialFact: A benchmark for Fine-grained Dialogue Fact Verification
• TASE: Token Awareness and Structured Evaluation for Multilingual Language Models
• Bench-2-CoP: Can We Trust Benchmarking for EU AI Compliance?
• ReasoningTrack: Chain-of-Thought Reasoning for Long-term Vision-Language Tracking
• EncQA: Benchmarking Vision-Language Models on Visual Encodings for Charts
• TRAIL: Joint Inference and Refinement of Knowledge Graphs with Large Language Models
• Are Large Language Models Dynamic Treatment Planners? An In Silico Study from a Prior Knowledge Injection Angle
• Decoding the Multimodal Maze: A Systematic Review on the Adoption of Explainability in Multimodal Attention-based Models
• StackPilot: Autonomous Function Agents for Scalable and Environment-Free Code Execution
• ReasoningGuard: Safeguarding Large Reasoning Models with Inference-time Safety Aha Moments
• AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization
• Experimental Analysis of Productive Interaction Strategy with ChatGPT: User Study on Function and Project-level Code Generation Tasks
• Enhancing Serendipity Recommendation System by Constructing Dynamic User Knowledge Graphs with Large Language Models
• LLMs are Single-threaded Reasoners: Demystifying the Working Mechanism of Soft Thinking
• Towards Trustworthy Multimodal Moderation via Policy-Aligned Reasoning and Hierarchical Labeling
• On the Evaluation of Large Language Models in Multilingual Vulnerability Repair
• Simple Methods Defend RAG Systems Well Against Real-World Attacks
• Everyone Contributes! Incentivizing Strategic Cooperation in Multi-LLM Systems via Sequential Public Goods Games
• Evaluating Position Bias in Large Language Model Recommendations
• From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning
• A Confidence-Diversity Framework for Calibrating AI Judgement in Accessible Qualitative Coding Tasks
• TIBSTC-CoT: A Multi-Domain Instruction Dataset for Chain-of-Thought Reasoning in Language Models
• Accelerating LLM Reasoning via Early Rejection with Partial Reward Modeling
• ProCut: LLM Prompt Compression via Attribution Estimation
• Sacred or Synthetic? Evaluating LLM Reliability and Abstention for Religious Questions
• DeepVIS: Bridging Natural Language and Data Visualization Through Step-wise Reasoning
• How Far Are LLMs from Symbolic Planners? An NLP-Based Perspective
• Better Call Claude: Can LLMs Detect Changes of Writing Style?
• SynAdapt: Learning Adaptive Reasoning in Large Language Models via Synthetic Continuous Chain-of-Thought
• The Missing Parts: Augmenting Fact Verification with Half-Truth Detection
• Thinking Machines: Mathematical Reasoning in the Age of LLMs
• Diagnostic Accuracy of Open-Source Vision-Language Models on Diverse Medical Imaging Tasks
• GanitBench: A bi-lingual benchmark for evaluating mathematical reasoning in Vision Language Models
• MemoCue: Empowering LLM-Based Agents for Human Memory Recall via Strategy-Guided Querying
• MPCC: A Novel Benchmark for Multimodal Planning with Complex Constraints in Multimodal Large Language Models
• Failures Are the Stepping Stones to Success: Enhancing Few-Shot In-Context Learning by Leveraging Negative Samples
• Automatically discovering heuristics in a complex SAT solver with large language models
• An Explainable Emotion Alignment Framework for LLM-Empowered Agent in Metaverse Service Ecosystem
• ChemDFM-R: A Chemical Reasoning LLM Enhanced with Atomized Chemical Knowledge
• Post-Training Large Language Models via Reinforcement Learning from Self-Feedback
• Introducing HALC: A general pipeline for finding optimal prompting strategies for automated coding with LLMs in the computational social sciences
• Can large language models assist choice modelling? Insights into prompting strategies and current models capabilities

Discussions

• Comes from Kojima et al, 2022 arxiv.org/abs/2205.11916 [bsky, 12 points, 1 comments]
• It's effectively a prompting trick - it started out as "think step by step" but now it's baked into the models (with special reinforcement learning to help them come up with better thinking traces bas [bsky, 4 points, 1 comments]
• Large Language Models are Zero-Shot Reasoners: Let's think step by step [hn, 4 points, 0 comments]
• Large Language Models are Zero-Shot Reasoners [lobsters, 4 points, 0 comments]
• ב arxiv.org/pdf/2205.11916 מראים שאפשר פשוט לבקש תעשה דרך. וב arxiv.org/pdf/2310.01714 מראים שאפשר לשפר תוצאות אם מבקשים "להיזכר במקרים דומים ואז לפתור" מה שדי מקרב אותנו לפרדיגמת ה-ReACT של סוכנים. א [bsky, 3 points, 2 comments]
• arxiv.org/abs/2205.11916 [bsky, 2 points, 0 comments]
• IIRC it all started here: arxiv.org/abs/2205.11916 "CoT reasoning" all descended from the observation that putting "let's think step by step" at the end of prompts tended to give much better answers, [bsky, 2 points, 1 comments]
• Large Language Models Are Zero-Shot Reasoners [hn, 1 points, 0 comments]
• [2205.11916] Large Language Models are Zero-Shot Reasoners | arXiv | Cornell University arxiv.org/abs/2205.11916 [bsky, 0 points, 1 comments]
• A veces es cuestión de plantearle al modelo el problema que queremos resolver y decirle "Pensemos este problema paso a paso" para que funcione, como se comenta en este artículo (Kojima et al, 2022). [bsky, 0 points, 1 comments]

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