{"total":85,"rows":[{"id":"2312.11970","title":"Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives","created":"2023-12-19","cited_by_count":257,"area":"agents"},{"id":"2305.18323","title":"ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models","created":"2023-05-23","cited_by_count":184,"area":"lr"},{"id":"2304.03279","title":"Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark","created":"2023-04-06","cited_by_count":182,"area":null},{"id":"2310.03094","title":"Large Language Model Cascades with Mixture of Thoughts Representations for Cost-efficient Reasoning","created":"2023-10-04","cited_by_count":143,"area":"lr"},{"id":"2305.13144","title":"Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline","created":"2023-05-22","cited_by_count":86,"area":"inference"},{"id":"2408.02479","title":"From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future","created":"2024-08-05","cited_by_count":86,"area":"agents"},{"id":"2405.09798","title":"Many-Shot In-Context Learning in Multimodal Foundation Models","created":"2024-05-16","cited_by_count":67,"area":null},{"id":"2408.13457","title":"Make Every Penny Count: Difficulty-Adaptive Self-Consistency for Cost-Efficient Reasoning","created":"2024-08-24","cited_by_count":63,"area":"lr"},{"id":"2308.12060","title":"FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering","created":"2023-08-23","cited_by_count":51,"area":null},{"id":"2310.08582","title":"Tree-Planner: Efficient Close-loop Task Planning with Large Language Models","created":"2023-10-12","cited_by_count":48,"area":"lr"},{"id":"2305.15035","title":"Self-ICL: Zero-Shot In-Context Learning with Self-Generated Demonstrations","created":"2023-05-24","cited_by_count":42,"area":"synth"},{"id":"2312.11813","title":"Urban Generative Intelligence (UGI): A Foundational Platform for Agents in Embodied City Environment","created":"2023-12-19","cited_by_count":37,"area":"agents"},{"id":"2503.03064","title":"Improving LLM-as-a-Judge Inference with the Judgment Distribution","created":"2025-03-04","cited_by_count":37,"area":null},{"id":"2410.21286","title":"OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents","created":"2024-10-11","cited_by_count":35,"area":"agents"},{"id":"2308.16361","title":"Large Language Models as Data Preprocessors","created":"2023-08-30","cited_by_count":34,"area":null},{"id":"2402.11597","title":"Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?","created":"2024-02-18","cited_by_count":30,"area":null},{"id":"2404.02319","title":"Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization","created":"2024-04-02","cited_by_count":30,"area":"rag"},{"id":"2310.07088","title":"Diversity of Thought Improves Reasoning Abilities of LLMs","created":"2023-10-11","cited_by_count":25,"area":"lr"},{"id":"2310.19660","title":"Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck","created":"2023-10-30","cited_by_count":22,"area":null},{"id":"2405.03547","title":"Position: Leverage Foundational Models for Black-Box Optimization","created":"2024-05-06","cited_by_count":21,"area":null},{"id":"2409.05001","title":"A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement","created":"2024-09-08","cited_by_count":21,"area":null},{"id":"2312.01678","title":"Jellyfish: A Large Language Model for Data Preprocessing","created":"2023-12-04","cited_by_count":20,"area":null},{"id":"2311.08154","title":"Just Ask One More Time! Self-Agreement Improves Reasoning of Language Models in (Almost) All Scenarios","created":"2023-11-14","cited_by_count":18,"area":"lr"},{"id":"2309.00384","title":"BatchPrompt: Accomplish more with less","created":"2023-09-01","cited_by_count":16,"area":null},{"id":"2312.03987","title":"Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration","created":"2023-12-07","cited_by_count":15,"area":null},{"id":"2402.06598","title":"CigaR: Cost-efficient Program Repair with LLMs","created":"2024-02-09","cited_by_count":15,"area":null},{"id":"2503.18599","title":"Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization","created":"2025-03-24","cited_by_count":14,"area":"inference"},{"id":"2507.08944","title":"Optimizing Sequential Multi-Step Tasks with Parallel LLM Agents","created":"2025-07-11","cited_by_count":14,"area":"agents"},{"id":"2407.02211","title":"PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning","created":"2024-07-02","cited_by_count":13,"area":null},{"id":"2402.14833","title":"CliqueParcel: An Approach For Batching LLM Prompts That Jointly Optimizes Efficiency And Faithfulness","created":"2024-02-17","cited_by_count":12,"area":null},{"id":"2311.06330","title":"Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations","created":"2023-11-10","cited_by_count":11,"area":"agents"},{"id":"2411.01643","title":"EcoAct: Economic Agent Determines When to Register What Action","created":"2024-11-03","cited_by_count":11,"area":"agents"},{"id":"2609.27678","title":"Same Scores, Different Decisions: Evaluating JEV and Language Models for Legal Document Understanding","created":"2026-09-23","cited_by_count":11,"area":null},{"id":"2402.17717","title":"AmbigNLG: Addressing Task Ambiguity in Instruction for NLG","created":"2024-02-27","cited_by_count":10,"area":null},{"id":"2405.13326","title":"Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning","created":"2024-05-22","cited_by_count":9,"area":"synth"},{"id":"2305.10865","title":"Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning","created":"2023-05-18","cited_by_count":8,"area":"agents"},{"id":"2411.03883","title":"MEG: Medical Knowledge-Augmented Large Language Models for Question Answering","created":"2024-11-06","cited_by_count":6,"area":null},{"id":"2504.07357","title":"Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction","created":"2025-04-10","cited_by_count":6,"area":"lr"},{"id":"2305.11862","title":"Reducing Sequence Length by Predicting Edit Operations with Large Language Models","created":"2023-05-19","cited_by_count":5,"area":null},{"id":"2402.07744","title":"Towards Unified Alignment Between Agents, Humans, and Environment","created":"2024-02-12","cited_by_count":5,"area":"agents"},{"id":"2406.10786","title":"Evaluating LLMs with Multiple Problems at once","created":"2024-06-16","cited_by_count":5,"area":null},{"id":"2505.09027","title":"Tests as Prompt: A Test-Driven-Development Benchmark for LLM Code Generation","created":"2025-05-13","cited_by_count":5,"area":null},{"id":"2510.10138","title":"Hybrid OCR-LLM Framework for Enterprise-Scale Document Information Extraction Under Copy-heavy Task","created":"2025-10-11","cited_by_count":5,"area":null},{"id":"2511.19830","title":"Beyond Relational: Semantic-Aware Multi-Modal Analytics with LLM-Native Query Optimization","created":"2025-11-25","cited_by_count":5,"area":null},{"id":"2602.01472","title":"ConPress: Learning Efficient Reasoning from Multi-Question Contextual Pressure","created":"2026-02-01","cited_by_count":4,"area":"lr"},{"id":"2408.00884","title":"Hybrid Querying Over Relational Databases and Large Language Models","created":"2024-08-01","cited_by_count":3,"area":null},{"id":"2410.01724","title":"Auto-Demo Prompting: Leveraging Generated Outputs as Demonstrations for Enhanced Batch Prompting","created":"2024-10-02","cited_by_count":3,"area":null},{"id":"2410.12444","title":"Augmenting Compliance-Guaranteed Customer Service Chatbots: Context-Aware Knowledge Expansion with Large Language Models","created":"2024-10-16","cited_by_count":3,"area":null},{"id":"2502.18791","title":"Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs","created":"2025-02-26","cited_by_count":3,"area":null},{"id":"2503.02756","title":"BatchGEMBA: Token-Efficient Machine Translation Evaluation with Batched Prompting and Prompt Compression","created":"2025-03-04","cited_by_count":3,"area":"inference"},{"id":"2504.21773","title":"MAC-Tuning: LLM Multi-Compositional Problem Reasoning with Enhanced Knowledge Boundary Awareness","created":"2025-04-30","cited_by_count":3,"area":"lr"},{"id":"2506.18728","title":"PARALLELPROMPT: Extracting Parallelism from Large Language Model Queries","created":"2025-06-23","cited_by_count":3,"area":"inference"},{"id":"2508.17803","title":"DRQA: Dynamic Reasoning Quota Allocation for Controlling Overthinking in Reasoning Large Language Models","created":"2025-08-25","cited_by_count":3,"area":"lr"},{"id":"2509.06806","title":"MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining","created":"2025-09-08","cited_by_count":3,"area":"lr"},{"id":"2403.11468","title":"CollagePrompt: A Benchmark for Budget-Friendly Visual Recognition with GPT-4V","created":"2024-03-18","cited_by_count":2,"area":null},{"id":"2403.13835","title":"SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees","created":"2024-03-11","cited_by_count":2,"area":null},{"id":"2503.03266","title":"LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law","created":"2025-03-05","cited_by_count":2,"area":null},{"id":"2503.15551","title":"Efficient but Vulnerable: Benchmarking and Defending LLM Batch Prompting Attack","created":"2025-03-18","cited_by_count":2,"area":null},{"id":"2506.19897","title":"Can LLMs Replace Humans During Code Chunking?","created":"2025-06-24","cited_by_count":2,"area":null},{"id":"2509.21732","title":"How Accurate Are LLMs at Multi-Question Answering on Conversational Transcripts?","created":"2025-09-26","cited_by_count":2,"area":null},{"id":"2603.22608","title":"Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length","created":"2026-03-23","cited_by_count":2,"area":null},{"id":"2310.18581","title":"Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE","created":"2023-10-28","cited_by_count":1,"area":"inference"},{"id":"2410.18701","title":"BATON: Enhancing Batch-wise Inference Efficiency for Large Language Models via Dynamic Re-batching","created":"2024-10-24","cited_by_count":1,"area":null},{"id":"2412.10587","title":"Evaluation of GPT-4o and GPT-4o-mini's Vision Capabilities for Compositional Analysis from Dried Solution Drops","created":"2024-12-13","cited_by_count":1,"area":null},{"id":"2509.00303","title":"Access Paths for Efficient Ordering with Large Language Models","created":"2025-08-30","cited_by_count":1,"area":null},{"id":"2511.04108","title":"Batch Prompting Suppresses Overthinking Reasoning Under Constraint: How Batch Prompting Suppresses Overthinking in Reasoning Models","created":"2025-11-06","cited_by_count":1,"area":"lr"},{"id":"2604.26209","title":"Breaking the Autoregressive Chain: Hyper-Parallel Decoding for Efficient LLM-Based Attribute Value Extraction","created":"2026-04-29","cited_by_count":1,"area":"inference"},{"id":"2607.23597","title":"HiTMS: A High-Throughput Multi-Stream Linguistic Steganography Framework","created":"2026-07-26","cited_by_count":1,"area":null},{"id":"2608.27038","title":"Cascaded Batch Prompting","created":"2026-08-27","cited_by_count":1,"area":null},{"id":"2506.21138","title":"Multi-Sample Prompting and Actor-Critic Prompt Optimization for Diverse Synthetic Data Generation","created":"2025-06-26","cited_by_count":0,"area":"alignment"},{"id":"2511.12010","title":"Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles","created":"2025-11-15","cited_by_count":0,"area":null},{"id":"2604.17512","title":"ONTO: A Token-Efficient Columnar Notation for LLM Input Optimization","created":"2026-04-19","cited_by_count":0,"area":null},{"id":"2604.23255","title":"Scalable LLM-based Coding of Dialogue in Healthcare Simulation: Balancing Coding Performance, Processing Time, and Environmental Impact","created":"2026-04-25","cited_by_count":0,"area":null},{"id":"2604.26351","title":"A Dual-Task Paradigm to Investigate Sentence Comprehension Strategies in Language Models","created":"2026-04-29","cited_by_count":0,"area":null},{"id":"2606.00285","title":"Model-Based Quality Assessment for Massively Multilingual Parallel Data","created":"2026-05-29","cited_by_count":0,"area":null},{"id":"2606.08617","title":"Cross-Source Reasoning-based Correction for Author Name Disambiguation","created":"2026-06-07","cited_by_count":0,"area":null},{"id":"2606.21098","title":"LLM-Based Multi-Reference Evaluation for Efficient and Robust Assessment of Phrase Break Annotations","created":"2026-06-19","cited_by_count":0,"area":null},{"id":"2607.20528","title":"PromptPack: Scaling LLM Annotation Agents for Online Recommendation","created":"2026-07-10","cited_by_count":0,"area":"agents"},{"id":"2608.02681","title":"Safety in Batches? Understanding and Mitigating Safety Failures in Batch Prompting","created":"2026-08-03","cited_by_count":0,"area":"safety"},{"id":"2608.12627","title":"EgoCITE: Context-Augmented Indexing and Time-Aware Retrieval for Long-Horizon Egocentric Memory","created":"2026-08-12","cited_by_count":0,"area":"agents"},{"id":"2608.14737","title":"Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews","created":"2026-08-13","cited_by_count":0,"area":null},{"id":"2609.10810","title":"Larger Context Window, Fewer Overcorrections: Optimizing Prompts and Batching for Minimal-Edit Grammatical Error Correction","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.22620","title":"Splitting Documents at Lower Cost: Multi-Split Boundary Decisions for LLM-Based Page Stream Segmentation","created":"2026-09-18","cited_by_count":0,"area":null},{"id":"2609.35250","title":"SCBO: Semantically Coherent Batching and Ordering for LLM-Based Social Surveys","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.35824","title":"Reliable but Design-Sensitive: Instrument Uncertainty in LLM Annotation","created":"2026-09-22","cited_by_count":0,"area":null}],"next_cursor":null}