{"total":33,"rows":[{"id":"2407.21792","title":"Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?","created":"2024-07-31","cited_by_count":61,"area":null},{"id":"2504.04715","title":"Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs","created":"2025-04-07","cited_by_count":38,"area":null},{"id":"2407.04965","title":"Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression","created":"2024-07-06","cited_by_count":33,"area":null},{"id":"2407.09141","title":"Accuracy is Not All You Need","created":"2024-07-12","cited_by_count":31,"area":null},{"id":"2409.11055","title":"Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant","created":"2024-09-17","cited_by_count":14,"area":null},{"id":"2505.01043","title":"Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities","created":"2025-05-02","cited_by_count":11,"area":null},{"id":"2504.10816","title":"CSPLADE: Learned Sparse Retrieval with Causal Language Models","created":"2025-04-15","cited_by_count":10,"area":null},{"id":"2405.17512","title":"On Fairness of Low-Rank Adaptation of Large Models","created":"2024-05-27","cited_by_count":9,"area":null},{"id":"2508.19432","title":"Quantized but Deceptive? A Multi-Dimensional Truthfulness Evaluation of Quantized LLMs","created":"2025-08-26","cited_by_count":9,"area":null},{"id":"2502.00840","title":"Activation Approximations Can Incur Safety Vulnerabilities Even in Aligned LLMs: Comprehensive Analysis and Defense","created":"2025-02-02","cited_by_count":7,"area":null},{"id":"2602.06181","title":"Investigating Social Bias Changes in Quantized Language Models","created":"2026-02-05","cited_by_count":5,"area":null},{"id":"2606.15980","title":"Do Activation Monitors Survive Model Updates? Benchmarking, Predicting, and Repairing Activation-Monitor Staleness","created":"2026-06-14","cited_by_count":5,"area":null},{"id":"2508.18088","title":"How Quantization Shapes Bias in Large Language Models","created":"2025-08-25","cited_by_count":4,"area":null},{"id":"2505.13963","title":"Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall","created":"2025-05-20","cited_by_count":3,"area":null},{"id":"2506.20251","title":"Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models","created":"2025-06-25","cited_by_count":2,"area":null},{"id":"2507.17725","title":"On the Interaction of Compressibility and Adversarial Robustness","created":"2025-07-23","cited_by_count":2,"area":null},{"id":"2509.00096","title":"Pruning Weights but Not Truth: Safeguarding Truthfulness While Pruning LLMs","created":"2025-08-27","cited_by_count":2,"area":null},{"id":"2605.08137","title":"Weight Pruning Amplifies Bias: A Multi-Method Study of Compressed LLMs for Edge AI","created":"2026-05-02","cited_by_count":2,"area":null},{"id":"2608.11981","title":"Benchmarking Trustworthiness of SLMs: Pre-trained vs. Compressed","created":"2026-08-12","cited_by_count":2,"area":null},{"id":"2506.08487","title":"Beyond Bias Scores: Unmasking Vacuous Neutrality in Small Language Models","created":"2025-06-10","cited_by_count":1,"area":null},{"id":"2601.12033","title":"Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection","created":"2026-01-17","cited_by_count":1,"area":null},{"id":"2606.10154","title":"Quality Is Not a Safety Proxy Under Quantization","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2608.19670","title":"The Asymmetric Harms of LLM Compression","created":"2026-08-20","cited_by_count":1,"area":null},{"id":"2508.13533","title":"Models in the Same Family are NOT Trust-Equivalent","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2509.15206","title":"Fair-GPTQ: Bias-Aware Quantization for Large Language Models","created":"2025-09-18","cited_by_count":0,"area":null},{"id":"2511.22099","title":"Decomposed Trust: Privacy, Adversarial Robustness, Ethics, and Fairness in Low-Rank LLMs","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2511.22483","title":"Enhancing Trustworthiness with Mixed Precision: Benchmarks, Opportunities, and Challenges","created":"2025-11-27","cited_by_count":0,"area":null},{"id":"2607.10855","title":"Reliability Scaling Laws for Quantized Large Language Models","created":"2026-07-12","cited_by_count":0,"area":null},{"id":"2608.02940","title":"When Compression Scores Cannot Decide: Information Boundaries for Group-Robust LLM Pruning","created":"2026-08-03","cited_by_count":0,"area":null},{"id":"2608.25977","title":"When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs","created":"2026-08-26","cited_by_count":0,"area":null},{"id":"2609.33898","title":"No Free Efficiency: Revisiting the Trade-off Between Training Efficiency and Model Vulnerability","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.38106","title":"Pruning for Efficiency, Paying in Fairness: Demographic Disparities in Pruned Speech-LLMs","created":"2026-09-29","cited_by_count":0,"area":null},{"id":"2610.02462","title":"Capability Scaling-Down Laws for LLM Compression","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}