{"total":94,"rows":[{"id":"2412.05579","title":"LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods","created":"2024-12-07","cited_by_count":465,"area":null},{"id":"2406.07791","title":"Judging the Judges: A Systematic Study of Position Bias in LLM-as-a-Judge","created":"2024-06-12","cited_by_count":269,"area":null},{"id":"2402.00367","title":"Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration","created":"2024-02-01","cited_by_count":180,"area":null},{"id":"2403.16950","title":"Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators","created":"2024-03-25","cited_by_count":149,"area":"alignment"},{"id":"2407.12404","title":"Analyzing the Generalization and Reliability of Steering Vectors","created":"2024-07-17","cited_by_count":143,"area":"mechinterp"},{"id":"2502.02533","title":"Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies","created":"2025-02-04","cited_by_count":113,"area":"agents"},{"id":"2401.06766","title":"Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements","created":"2024-01-12","cited_by_count":83,"area":null},{"id":"2405.01976","title":"Conformal Prediction for Natural Language Processing: A Survey","created":"2024-05-03","cited_by_count":53,"area":null},{"id":"2507.16076","title":"The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models","created":"2025-07-21","cited_by_count":42,"area":null},{"id":"2405.14394","title":"Instruction Tuning With Loss Over Instructions","created":"2024-05-23","cited_by_count":40,"area":null},{"id":"2403.08819","title":"Thermometer: Towards Universal Calibration for Large Language Models","created":"2024-02-20","cited_by_count":38,"area":null},{"id":"2410.15393","title":"CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges","created":"2024-10-20","cited_by_count":34,"area":null},{"id":"2502.14074","title":"Investigating Non-Transitivity in LLM-as-a-Judge","created":"2025-02-19","cited_by_count":34,"area":"agents"},{"id":"2406.03441","title":"Cycles of Thought: Measuring LLM Confidence through Stable Explanations","created":"2024-06-05","cited_by_count":29,"area":"lr"},{"id":"2405.02743","title":"Beyond Performance: Quantifying and Mitigating Label Bias in LLMs","created":"2024-05-04","cited_by_count":25,"area":null},{"id":"2406.11370","title":"Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments","created":"2024-06-17","cited_by_count":25,"area":"alignment"},{"id":"2410.10414","title":"On Calibration of LLM-based Guard Models for Reliable Content Moderation","created":"2024-10-14","cited_by_count":23,"area":"safety"},{"id":"2312.04021","title":"A Study on the Calibration of In-context Learning","created":"2023-12-07","cited_by_count":22,"area":null},{"id":"2502.00330","title":"From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation","created":"2025-02-01","cited_by_count":21,"area":"lr"},{"id":"2504.06969","title":"Towards LLMs Robustness to Changes in Prompt Format Styles","created":"2025-04-09","cited_by_count":21,"area":null},{"id":"2602.06176","title":"Large Language Model Reasoning Failures","created":"2026-02-05","cited_by_count":21,"area":"lr"},{"id":"2502.10709","title":"An Empirical Analysis of Uncertainty in Large Language Model Evaluations","created":"2025-02-15","cited_by_count":20,"area":null},{"id":"2509.10704","title":"Maestro: Self-Improving Text-to-Image Generation via Agent Orchestration","created":"2025-09-12","cited_by_count":19,"area":"agents"},{"id":"2310.12774","title":"Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning","created":"2023-10-19","cited_by_count":17,"area":null},{"id":"2510.07475","title":"MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference","created":"2025-10-08","cited_by_count":17,"area":"agents"},{"id":"2401.12406","title":"Enhancing In-context Learning via Linear Probe Calibration","created":"2024-01-22","cited_by_count":16,"area":"mechinterp"},{"id":"2312.01082","title":"A Survey on Stability of Learning with Limited Labelled Data and its Sensitivity to the Effects of Randomness","created":"2023-12-02","cited_by_count":14,"area":null},{"id":"2406.15948","title":"Teaching LLMs to Abstain across Languages via Multilingual Feedback","created":"2024-06-22","cited_by_count":14,"area":null},{"id":"2312.13772","title":"Large Language Models are Miscalibrated In-Context Learners","created":"2023-12-21","cited_by_count":12,"area":null},{"id":"2411.02988","title":"Confidence Calibration of Classifiers with Many Classes","created":"2024-11-05","cited_by_count":11,"area":null},{"id":"2506.00519","title":"CausalAbstain: Enhancing Multilingual LLMs with Causal Reasoning for Trustworthy Abstention","created":"2025-05-31","cited_by_count":10,"area":"lr"},{"id":"2508.11383","title":"When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs","created":"2025-08-15","cited_by_count":10,"area":null},{"id":"2403.09488","title":"Rectifying Demonstration Shortcut in In-Context Learning","created":"2024-03-14","cited_by_count":9,"area":null},{"id":"2406.11890","title":"Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning","created":"2024-06-14","cited_by_count":9,"area":null},{"id":"2406.16535","title":"Token-based Decision Criteria Are Suboptimal in In-context Learning","created":"2024-06-24","cited_by_count":9,"area":null},{"id":"2512.22245","title":"Calibrating LLM Judges: Linear Probes for Fast and Reliable Uncertainty Estimation","created":"2025-12-23","cited_by_count":9,"area":"mechinterp"},{"id":"2505.19773","title":"What Really Matters in Many-Shot Attacks? An Empirical Study of Long-Context Vulnerabilities in LLMs","created":"2025-05-26","cited_by_count":8,"area":"safety"},{"id":"2410.11677","title":"Understanding Likelihood Over-optimisation in Direct Alignment Algorithms","created":"2024-10-15","cited_by_count":7,"area":"alignment"},{"id":"2504.03159","title":"Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction","created":"2025-04-04","cited_by_count":7,"area":null},{"id":"2507.03279","title":"Conformal Information Pursuit for Interactively Guiding Large Language Models","created":"2025-07-04","cited_by_count":7,"area":null},{"id":"2406.13131","title":"When Parts Are Greater Than Sums: Individual LLM Components Can Outperform Full Models","created":"2024-06-19","cited_by_count":6,"area":null},{"id":"2406.17095","title":"Attention Instruction: Amplifying Attention in the Middle via Prompting","created":"2024-06-24","cited_by_count":6,"area":null},{"id":"2604.10925","title":"From Words to Widgets for Controllable LLM Generation","created":"2026-04-13","cited_by_count":5,"area":null},{"id":"2504.11381","title":"RankAlign: A Ranking View of the Generator-Validator Gap in Large Language Models","created":"2025-04-15","cited_by_count":4,"area":null},{"id":"2504.18346","title":"Comparing Uncertainty Measurement and Mitigation Methods for Large Language Models: A Systematic Review","created":"2025-04-25","cited_by_count":4,"area":null},{"id":"2502.12464","title":"SafeRoute: Adaptive Model Selection for Efficient and Accurate Safety Guardrails in Large Language Models","created":"2025-02-18","cited_by_count":3,"area":"safety"},{"id":"2510.00263","title":"Judging with Confidence: Calibrating Autoraters to Preference Distributions","created":"2025-09-30","cited_by_count":3,"area":"alignment"},{"id":"2601.13284","title":"Balancing Classification and Calibration Performance in Decision-Making LLMs via Calibration Aware Reinforcement Learning","created":"2026-01-19","cited_by_count":3,"area":null},{"id":"2602.04297","title":"Revisiting Prompt Sensitivity in Large Language Models for Text Classification: The Role of Prompt Underspecification","created":"2026-02-04","cited_by_count":3,"area":null},{"id":"2605.30227","title":"Unifying Temporal and Structural Credit Assignment in LLM-Based Multi-Agent Prompt Optimization","created":"2026-05-28","cited_by_count":3,"area":"lr"},{"id":"2609.32160","title":"Typed Decision Models: An Early Evidence Audit and Evaluation Checklist","created":"2026-09-26","cited_by_count":3,"area":null},{"id":"2405.07623","title":"Optimizing Class-Level Probability Reweighting Coefficients for Equitable Prompting Accuracy","created":"2024-05-13","cited_by_count":2,"area":null},{"id":"2409.10044","title":"Benchmarking Large Language Model Uncertainty for Prompt Optimization","created":"2024-09-16","cited_by_count":2,"area":"lr"},{"id":"2412.19018","title":"Let the Fuzzy Rule Speak: Enhancing In-context Learning Debiasing with Interpretability","created":"2024-12-26","cited_by_count":2,"area":null},{"id":"2501.15708","title":"StaICC: Standardized Evaluation for Classification Task in In-context Learning","created":"2025-01-27","cited_by_count":2,"area":null},{"id":"2506.12796","title":"Surprise Calibration for Better In-Context Learning","created":"2025-06-15","cited_by_count":2,"area":null},{"id":"2507.08038","title":"AblationBench: Evaluating Automated Planning of Ablations in Empirical AI Research","created":"2025-07-09","cited_by_count":2,"area":"agents"},{"id":"2511.14385","title":"Mitigating Label Length Bias in Large Language Models","created":"2025-11-18","cited_by_count":2,"area":null},{"id":"2511.21709","title":"Quantifying and Mitigating Selection Bias in LLMs: A Transferable LoRA Fine-Tuning and Efficient Majority Voting Approach","created":"2025-11-17","cited_by_count":2,"area":null},{"id":"2402.17671","title":"Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models","created":"2024-02-27","cited_by_count":1,"area":null},{"id":"2502.04997","title":"Aligning Black-box Language Models with Human Judgments","created":"2025-02-07","cited_by_count":1,"area":null},{"id":"2503.05157","title":"Ensemble Debiasing Across Class and Sample Levels for Fairer Prompting Accuracy","created":"2025-03-07","cited_by_count":1,"area":null},{"id":"2505.23783","title":"Boosting In-Context Learning in LLMs Through the Lens of Classical Supervised Learning","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2509.21926","title":"PANICL: Mitigating Over-Reliance on Single Prompt in Visual In-Context Learning","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2511.16478","title":"Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation","created":"2025-11-20","cited_by_count":1,"area":null},{"id":"2512.23489","title":"The Gaining Paths to Investment Success: Information-Driven LLM Graph Reasoning for Venture Capital Prediction","created":"2025-12-29","cited_by_count":1,"area":"lr"},{"id":"2602.12445","title":"RBCorr: Response Bias Correction in Language Models","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2603.15134","title":"Confusion-Aware In-Context-Learning for Vision-Language Models in Robotic Manipulation","created":"2026-03-16","cited_by_count":1,"area":"mllm"},{"id":"2604.05397","title":"Confidence Should Be Calibrated More Than One Turn Deep","created":"2026-04-07","cited_by_count":1,"area":null},{"id":"2606.14728","title":"FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty","created":"2026-06-01","cited_by_count":1,"area":"mllm"},{"id":"2608.06409","title":"Separating Decision-Rule Misalignment from Readout-Coverage Limitations in Speech Language Models","created":"2026-08-03","cited_by_count":1,"area":null},{"id":"2608.08869","title":"Are LLMs Positionally Consistent Ordinal Classifiers? A Systematic Evaluation","created":"2026-08-09","cited_by_count":1,"area":null},{"id":"2609.14762","title":"TriCalRAG: A Three-Strategy, Retrieval-Augmented Benchmark for On-Premise LLM-Based Root Cause Analysis in AIOps","created":"2026-09-13","cited_by_count":1,"area":"rag"},{"id":"2610.00831","title":"AnyJev Technical Report","created":"2026-09-30","cited_by_count":1,"area":null},{"id":"2407.20654","title":"Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian","created":"2024-07-30","cited_by_count":0,"area":null},{"id":"2410.02210","title":"Calibrate to Discriminate: Improve In-Context Learning with Label-Free Comparative Inference","created":"2024-10-03","cited_by_count":0,"area":null},{"id":"2410.18764","title":"Task Calibration: Calibrating Large Language Models on Inference Tasks","created":"2024-10-24","cited_by_count":0,"area":null},{"id":"2505.14233","title":"Mechanistic Fine-tuning for In-context Learning","created":"2025-05-20","cited_by_count":0,"area":null},{"id":"2505.16522","title":"Large Language Models Are Still Misled by Simple Bias Ensembles","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2510.17727","title":"Enabling Fine-Grained Operating Points for Black-Box LLMs","created":"2025-10-20","cited_by_count":0,"area":null},{"id":"2602.05524","title":"AI Agent Systems for Supply Chains: Structured Decision Prompts and Memory Retrieval","created":"2026-02-05","cited_by_count":0,"area":"agents"},{"id":"2605.23180","title":"Self-Improving In-Context Learning","created":"2026-05-22","cited_by_count":0,"area":null},{"id":"2605.29816","title":"Harnessing non-adversarial robustness in large language models","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2607.18259","title":"Probabilistic Concept-Aware Steering for Trustworthy LLM Inference","created":"2026-05-15","cited_by_count":0,"area":"mechinterp"},{"id":"2607.21616","title":"Lost in Context: Addressing Context Anxiety in Large Language Models","created":"2026-05-29","cited_by_count":0,"area":"lr"},{"id":"2607.27405","title":"Benchmarking LLM Competence on Logical Inference over Probability Operators","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2607.27766","title":"Gradient-free Task-Conditioned Retrieval for On-Device In-Context Learning","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.19208","title":"When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models","created":"2026-06-12","cited_by_count":0,"area":"mllm"},{"id":"2608.20349","title":"Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality","created":"2026-06-16","cited_by_count":0,"area":null},{"id":"2609.29807","title":"CORDIAL: Calibrating Ordinal LLM Outputs from Few Labels","created":"2026-09-24","cited_by_count":0,"area":null},{"id":"2609.33851","title":"Rethinking Contextualization by Reinterpreting Attention Head Channels","created":"2026-09-27","cited_by_count":0,"area":"lr"},{"id":"2610.02976","title":"Relevant Evidence Decoding for Audio-Visual Hallucination Mitigation","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.16839","title":"Label Collapse in Diffusion Language Model In-Context Learning","created":"2026-10-12","cited_by_count":0,"area":"diffusion"},{"id":"2610.18233","title":"Consider, Then Choose: Order-Invariant Decisions in System One Models","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}