{"total":39,"rows":[{"id":"2309.03882","title":"Large Language Models Are Not Robust Multiple Choice Selectors","created":"2023-09-07","cited_by_count":486,"area":null},{"id":"2308.05374","title":"Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment","created":"2023-08-10","cited_by_count":401,"area":null},{"id":"2306.04891","title":"In-Context Learning through the Bayesian Prism","created":"2023-06-08","cited_by_count":83,"area":null},{"id":"2307.12375","title":"In-Context Learning Learns Label Relationships but Is Not Conventional Learning","created":"2023-07-23","cited_by_count":51,"area":null},{"id":"2311.08648","title":"Explore Spurious Correlations at the Concept Level in Language Models for Text Classification","created":"2023-11-15","cited_by_count":48,"area":null},{"id":"2311.08993","title":"When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks","created":"2023-11-15","cited_by_count":37,"area":null},{"id":"2311.00237","title":"The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis","created":"2023-11-01","cited_by_count":34,"area":null},{"id":"2307.10558","title":"Instruction-following Evaluation through Verbalizer Manipulation","created":"2023-07-20","cited_by_count":30,"area":null},{"id":"2310.00297","title":"Understanding In-Context Learning from Repetitions","created":"2023-09-30","cited_by_count":29,"area":null},{"id":"2405.02743","title":"Beyond Performance: Quantifying and Mitigating Label Bias in LLMs","created":"2024-05-04","cited_by_count":25,"area":null},{"id":"2311.07811","title":"In-context Learning Generalizes, But Not Always Robustly: The Case of Syntax","created":"2023-11-13","cited_by_count":22,"area":null},{"id":"2311.12997","title":"Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks","created":"2023-11-21","cited_by_count":22,"area":null},{"id":"2310.10266","title":"Generative Calibration for In-context Learning","created":"2023-10-16","cited_by_count":20,"area":null},{"id":"2406.03009","title":"Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models","created":"2024-06-05","cited_by_count":20,"area":null},{"id":"2312.11518","title":"User Modeling in the Era of Large Language Models: Current Research and Future Directions","created":"2023-12-11","cited_by_count":18,"area":null},{"id":"2310.17639","title":"In-Context Learning Dynamics with Random Binary Sequences","created":"2023-10-26","cited_by_count":13,"area":null},{"id":"2503.19598","title":"The Greatest Good Benchmark: Measuring LLMs' Alignment with Utilitarian Moral Dilemmas","created":"2025-03-25","cited_by_count":12,"area":null},{"id":"2410.22590","title":"Characterizing the Role of Similarity in the Property Inferences of Language Models","created":"2024-10-29","cited_by_count":11,"area":null},{"id":"2601.05106","title":"Token-Level LLM Collaboration via FusionRoute","created":"2026-01-08","cited_by_count":9,"area":null},{"id":"2411.02018","title":"Shortcut Learning in In-Context Learning: A Survey","created":"2024-11-04","cited_by_count":8,"area":null},{"id":"2506.06950","title":"What Makes a Good Natural Language Prompt?","created":"2025-06-07","cited_by_count":7,"area":null},{"id":"2407.15352","title":"MAVEN-Fact: A Large-scale Event Factuality Detection Dataset","created":"2024-07-22","cited_by_count":6,"area":null},{"id":"2410.05218","title":"Density estimation with LLMs: a geometric investigation of in-context learning trajectories","created":"2024-10-07","cited_by_count":6,"area":null},{"id":"2312.07476","title":"Comparable Demonstrations are Important in In-Context Learning: A Novel Perspective on Demonstration Selection","created":"2023-12-12","cited_by_count":5,"area":null},{"id":"2506.11111","title":"Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions","created":"2025-06-08","cited_by_count":5,"area":null},{"id":"2311.04046","title":"Reinforcement Learning Fine-tuning of Language Models is Biased Towards More Extractable Features","created":"2023-11-07","cited_by_count":3,"area":null},{"id":"2311.18194","title":"Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes","created":"2023-11-30","cited_by_count":3,"area":null},{"id":"2401.06640","title":"Experimental Contexts Can Facilitate Robust Semantic Property Inference in Language Models, but Inconsistently","created":"2024-01-12","cited_by_count":3,"area":null},{"id":"2606.30252","title":"Inoculation Adapters: Improved Selective Generalization of Capabilities with Fewer Surprising Backdoors","created":"2026-06-29","cited_by_count":3,"area":null},{"id":"2506.12796","title":"Surprise Calibration for Better In-Context Learning","created":"2025-06-15","cited_by_count":2,"area":null},{"id":"2511.06148","title":"Large Language Models Develop Novel Social Biases Through Adaptive Exploration","created":"2025-11-08","cited_by_count":2,"area":null},{"id":"2410.09349","title":"Inference and Verbalization Functions During In-Context Learning","created":"2024-10-12","cited_by_count":1,"area":null},{"id":"2410.16246","title":"Analyzing Context Contributions in LLM-based Machine Translation","created":"2024-10-21","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":"2509.20278","title":"Quantifying LLM Biases Across Instruction Boundary in Mixed Question Forms","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2501.07324","title":"Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring","created":"2025-01-13","cited_by_count":0,"area":null},{"id":"2602.11863","title":"In-Context Function Learning in Large Language Models","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2606.31844","title":"Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2609.07309","title":"LANTERN: Language Model Assessment on Noisy and Transformed Tasks for Understanding Error and Robustness Nuances","created":"2026-09-07","cited_by_count":0,"area":null}],"next_cursor":null}