{"total":114,"rows":[{"id":"2102.01454","title":"MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers","created":"2021-02-02","cited_by_count":470,"area":null},{"id":"2107.00061","title":"All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text","created":"2021-06-30","cited_by_count":287,"area":null},{"id":"2310.05130","title":"Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature","created":"2023-10-08","cited_by_count":287,"area":null},{"id":"2201.06796","title":"CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities","created":"2022-01-18","cited_by_count":249,"area":null},{"id":"2102.01672","title":"The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics","created":"2021-02-02","cited_by_count":239,"area":null},{"id":"2306.15895","title":"Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias","created":"2023-06-28","cited_by_count":232,"area":null},{"id":"2405.20362","title":"Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools","created":"2024-05-30","cited_by_count":176,"area":null},{"id":"2202.06935","title":"Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text","created":"2022-02-14","cited_by_count":155,"area":null},{"id":"2201.05337","title":"A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models","created":"2022-01-14","cited_by_count":129,"area":null},{"id":"2111.09509","title":"How much do language models copy from their training data? Evaluating linguistic novelty in text generation using RAVEN","created":"2021-11-18","cited_by_count":124,"area":null},{"id":"2210.15191","title":"Truncation Sampling as Language Model Desmoothing","created":"2022-10-27","cited_by_count":123,"area":null},{"id":"2305.18486","title":"A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets","created":"2023-05-29","cited_by_count":123,"area":null},{"id":"2206.07271","title":"Human heuristics for AI-generated language are flawed","created":"2022-06-15","cited_by_count":105,"area":null},{"id":"2212.09746","title":"Evaluating Human-Language Model Interaction","created":"2022-12-19","cited_by_count":92,"area":null},{"id":"2107.01294","title":"Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text","created":"2021-07-02","cited_by_count":89,"area":null},{"id":"2207.00939","title":"An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics","created":"2022-07-03","cited_by_count":74,"area":null},{"id":"2201.04723","title":"Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents","created":"2022-01-12","cited_by_count":62,"area":null},{"id":"2109.06379","title":"Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation","created":"2021-09-14","cited_by_count":60,"area":null},{"id":"2211.07634","title":"Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding","created":"2022-11-14","cited_by_count":54,"area":null},{"id":"2212.09739","title":"LENS: A Learnable Evaluation Metric for Text Simplification","created":"2022-12-19","cited_by_count":54,"area":null},{"id":"2305.11707","title":"What Comes Next? Evaluating Uncertainty in Neural Text Generators Against Human Production Variability","created":"2023-05-19","cited_by_count":51,"area":null},{"id":"2106.08367","title":"What Context Features Can Transformer Language Models Use?","created":"2021-06-15","cited_by_count":50,"area":null},{"id":"2106.07704","title":"Efficient (Soft) Q-Learning for Text Generation with Limited Good Data","created":"2021-06-14","cited_by_count":37,"area":null},{"id":"2212.14578","title":"MAUVE Scores for Generative Models: Theory and Practice","created":"2022-12-30","cited_by_count":36,"area":null},{"id":"2305.12421","title":"Evaluating Open-QA Evaluation","created":"2023-05-21","cited_by_count":35,"area":null},{"id":"2306.01985","title":"COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements","created":"2023-06-03","cited_by_count":34,"area":null},{"id":"2310.01693","title":"Closing the Curious Case of Neural Text Degeneration","created":"2023-10-02","cited_by_count":32,"area":null},{"id":"2105.11921","title":"Focus Attention: Promoting Faithfulness and Diversity in Summarization","created":"2021-05-25","cited_by_count":30,"area":null},{"id":"2305.18226","title":"HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis","created":"2023-05-26","cited_by_count":30,"area":null},{"id":"2204.00862","title":"CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation","created":"2022-04-02","cited_by_count":29,"area":null},{"id":"2205.01497","title":"Semantic Diversity in Dialogue with Natural Language Inference","created":"2022-05-03","cited_by_count":29,"area":null},{"id":"2203.15108","title":"A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation","created":"2022-03-28","cited_by_count":28,"area":null},{"id":"2302.13344","title":"Tailoring Language Generation Models under Total Variation Distance","created":"2023-02-26","cited_by_count":27,"area":null},{"id":"2410.04197","title":"CS4: Measuring the Creativity of Large Language Models Automatically by Controlling the Number of Story-Writing Constraints","created":"2024-10-05","cited_by_count":24,"area":null},{"id":"2311.09799","title":"How Far Can We Extract Diverse Perspectives from Large Language Models?","created":"2023-11-16","cited_by_count":22,"area":null},{"id":"2101.06561","title":"GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation","created":"2021-01-17","cited_by_count":20,"area":null},{"id":"2206.05395","title":"Why is constrained neural language generation particularly challenging?","created":"2022-06-11","cited_by_count":19,"area":null},{"id":"2303.04729","title":"Stealing the Decoding Algorithms of Language Models","created":"2023-03-08","cited_by_count":19,"area":null},{"id":"2105.12437","title":"The statistical advantage of automatic NLG metrics at the system level","created":"2021-05-26","cited_by_count":18,"area":null},{"id":"2305.12519","title":"DPIC: Decoupling Prompt and Intrinsic Characteristics for LLM Generated Text Detection","created":"2023-05-21","cited_by_count":18,"area":null},{"id":"2307.03749","title":"On the Efficacy of Sampling Adapters","created":"2023-07-07","cited_by_count":17,"area":null},{"id":"2406.15267","title":"Evaluating Diversity in Automatic Poetry Generation","created":"2024-06-21","cited_by_count":17,"area":null},{"id":"2305.08566","title":"NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist","created":"2023-05-15","cited_by_count":16,"area":null},{"id":"2305.16958","title":"MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies","created":"2023-05-26","cited_by_count":16,"area":null},{"id":"2305.14458","title":"Dancing Between Success and Failure: Edit-level Simplification Evaluation using SALSA","created":"2023-05-23","cited_by_count":15,"area":null},{"id":"2404.04204","title":"Social Skill Training with Large Language Models","created":"2024-04-05","cited_by_count":15,"area":null},{"id":"2510.01218","title":"Control the Temperature: Selective Sampling for Diverse and High-Quality LLM Outputs","created":"2025-09-20","cited_by_count":15,"area":null},{"id":"2203.03047","title":"Recent Advances in Neural Text Generation: A Task-Agnostic Survey","created":"2022-03-06","cited_by_count":14,"area":null},{"id":"2412.11506","title":"Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection","created":"2024-12-16","cited_by_count":14,"area":null},{"id":"2409.16914","title":"Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness","created":"2024-09-25","cited_by_count":13,"area":null},{"id":"2110.04400","title":"HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models","created":"2021-10-08","cited_by_count":12,"area":null},{"id":"2410.22318","title":"Online Detection of LLM-Generated Texts via Sequential Hypothesis Testing by Betting","created":"2024-10-29","cited_by_count":11,"area":null},{"id":"2509.15550","title":"DNA-DetectLLM: Unveiling AI-Generated Text via a DNA-Inspired Mutation-Repair Paradigm","created":"2025-09-19","cited_by_count":11,"area":null},{"id":"2204.05307","title":"Toward More Effective Human Evaluation for Machine Translation","created":"2022-04-11","cited_by_count":10,"area":null},{"id":"2207.12571","title":"Innovations in Neural Data-to-text Generation: A Survey","created":"2022-07-25","cited_by_count":10,"area":null},{"id":"2505.15337","title":"Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors","created":"2025-05-21","cited_by_count":10,"area":null},{"id":"2102.05638","title":"Generating Synthetic Text Data to Evaluate Causal Inference Methods","created":"2021-02-10","cited_by_count":9,"area":null},{"id":"2312.14856","title":"Turbulence: Systematically and Automatically Testing Instruction-Tuned Large Language Models for Code","created":"2023-12-22","cited_by_count":9,"area":null},{"id":"2506.18082","title":"Statistical Multicriteria Evaluation of LLM-Generated Text","created":"2025-06-22","cited_by_count":9,"area":null},{"id":"2205.07233","title":"Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy","created":"2022-05-15","cited_by_count":8,"area":null},{"id":"2406.16767","title":"The GPT-WritingPrompts Dataset: A Comparative Analysis of Character Portrayal in Short Stories","created":"2024-06-24","cited_by_count":8,"area":null},{"id":"2108.08485","title":"Language Model Augmented Relevance Score","created":"2021-08-19","cited_by_count":7,"area":null},{"id":"2410.03968","title":"Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies","created":"2024-10-04","cited_by_count":7,"area":null},{"id":"2606.08417","title":"Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs Distributional Metrics","created":"2026-06-07","cited_by_count":7,"area":null},{"id":"2102.06859","title":"Capturing Label Distribution: A Case Study in NLI","created":"2021-02-13","cited_by_count":6,"area":null},{"id":"2209.07518","title":"Distribution Aware Metrics for Conditional Natural Language Generation","created":"2022-09-15","cited_by_count":6,"area":null},{"id":"2305.10307","title":"FACE: Evaluating Natural Language Generation with Fourier Analysis of Cross-Entropy","created":"2023-05-17","cited_by_count":6,"area":null},{"id":"2109.02938","title":"Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT","created":"2021-09-07","cited_by_count":5,"area":null},{"id":"2111.08906","title":"Using Sampling to Estimate and Improve Performance of Automated Scoring Systems with Guarantees","created":"2021-11-17","cited_by_count":5,"area":null},{"id":"2112.08657","title":"Taming Repetition in Dialogue Generation","created":"2021-12-16","cited_by_count":5,"area":null},{"id":"2401.09150","title":"Bridging Research and Readers: A Multi-Modal Automated Academic Papers Interpretation System","created":"2024-01-17","cited_by_count":5,"area":null},{"id":"2111.08133","title":"Exploring Story Generation with Multi-task Objectives in Variational Autoencoders","created":"2021-11-15","cited_by_count":4,"area":null},{"id":"2205.03666","title":"Vector Representations of Idioms in Conversational Systems","created":"2022-05-07","cited_by_count":4,"area":null},{"id":"2210.13025","title":"On the Effectiveness of Automated Metrics for Text Generation Systems","created":"2022-10-24","cited_by_count":4,"area":null},{"id":"2406.07967","title":"Better than Random: Reliable NLG Human Evaluation with Constrained Active Sampling","created":"2024-06-12","cited_by_count":4,"area":null},{"id":"2511.11966","title":"On the Entropy Calibration of Language Models","created":"2025-11-15","cited_by_count":4,"area":null},{"id":"2103.07649","title":"Improving Diversity of Neural Text Generation via Inverse Probability Weighting","created":"2021-03-13","cited_by_count":3,"area":null},{"id":"2111.09991","title":"Sketch-based Creativity Support Tools using Deep Learning","created":"2021-11-19","cited_by_count":3,"area":null},{"id":"2112.05917","title":"Show, Write, and Retrieve: Entity-aware Article Generation and Retrieval","created":"2021-12-11","cited_by_count":3,"area":null},{"id":"2306.03866","title":"Correction of Errors in Preference Ratings from Automated Metrics for Text Generation","created":"2023-06-06","cited_by_count":3,"area":null},{"id":"2103.11151","title":"The Interplay of Task Success and Dialogue Quality: An in-depth Evaluation in Task-Oriented Visual Dialogues","created":"2021-03-20","cited_by_count":2,"area":null},{"id":"2108.01369","title":"How to Evaluate Your Dialogue Models: A Review of Approaches","created":"2021-08-03","cited_by_count":2,"area":null},{"id":"2108.03578","title":"Language Model Evaluation in Open-ended Text Generation","created":"2021-08-08","cited_by_count":2,"area":null},{"id":"2112.08048","title":"Dynamic Human Evaluation for Relative Model Comparisons","created":"2021-12-15","cited_by_count":2,"area":null},{"id":"2202.13151","title":"COMPASS: a Creative Support System that Alerts Novelists to the Unnoticed Missing Contents","created":"2022-02-26","cited_by_count":2,"area":null},{"id":"2212.04348","title":"Implicit causality in GPT-2: a case study","created":"2022-12-08","cited_by_count":2,"area":null},{"id":"2501.01741","title":"How Toxic Can You Get? Search-based Toxicity Testing for Large Language Models","created":"2025-01-03","cited_by_count":2,"area":null},{"id":"2504.05801","title":"From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM","created":"2025-04-08","cited_by_count":2,"area":null},{"id":"2204.08083","title":"AfriWOZ: Corpus for Exploiting Cross-Lingual Transferability for Generation of Dialogues in Low-Resource, African Languages","created":"2022-04-17","cited_by_count":1,"area":null},{"id":"2205.06871","title":"Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets","created":"2022-05-13","cited_by_count":1,"area":null},{"id":"2205.09838","title":"Why GANs are overkill for NLP","created":"2022-05-19","cited_by_count":1,"area":null},{"id":"2205.10967","title":"Computational Storytelling and Emotions: A Survey","created":"2022-05-23","cited_by_count":1,"area":null},{"id":"2208.05757","title":"A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deception","created":"2022-08-11","cited_by_count":1,"area":null},{"id":"2503.10676","title":"Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data","created":"2025-03-10","cited_by_count":1,"area":null},{"id":"2503.23576","title":"The Impact of Code-switched Synthetic Data Quality is Task Dependent: Insights from MT and ASR","created":"2025-03-30","cited_by_count":1,"area":null},{"id":"2603.27949","title":"EnsemJudge: Enhancing Reliability in Chinese LLM-Generated Text Detection through Diverse Model Ensembles","created":"2026-03-30","cited_by_count":1,"area":null},{"id":"2608.05741","title":"Once a Response, Always a Response: Detecting LLM-generated Text via Latent Prompt Restoration","created":"2026-08-06","cited_by_count":1,"area":null},{"id":"2104.05224","title":"Estimating Subjective Crowd-Evaluations as an Additional Objective to Improve Natural Language Generation","created":"2021-04-12","cited_by_count":0,"area":null},{"id":"2111.06913","title":"Visual Intelligence through Human Interaction","created":"2021-11-12","cited_by_count":0,"area":null},{"id":"2206.11219","title":"Understanding the Properties of Generated Corpora","created":"2022-06-22","cited_by_count":0,"area":null}],"next_cursor":"100"}