{"total":62,"rows":[{"id":"2102.01017","title":"Measuring and Improving Consistency in Pretrained Language Models","created":"2021-02-01","cited_by_count":364,"area":null},{"id":"2106.07139","title":"Pre-Trained Models: Past, Present and Future","created":"2021-06-14","cited_by_count":319,"area":null},{"id":"2109.06129","title":"Can Language Models Encode Perceptual Structure Without Grounding? A Case Study in Color","created":"2021-09-13","cited_by_count":119,"area":null},{"id":"2106.09231","title":"Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases","created":"2021-06-17","cited_by_count":107,"area":null},{"id":"2303.07274","title":"Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images","created":"2023-03-13","cited_by_count":80,"area":null},{"id":"2106.02902","title":"BERTnesia: Investigating the capture and forgetting of knowledge in BERT","created":"2021-06-05","cited_by_count":40,"area":null},{"id":"2201.12438","title":"Commonsense Knowledge Reasoning and Generation with Pre-trained Language Models: A Survey","created":"2022-01-28","cited_by_count":38,"area":null},{"id":"2104.08161","title":"Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema","created":"2021-04-16","cited_by_count":32,"area":null},{"id":"2306.05720","title":"Beyond Surface Statistics: Scene Representations in a Latent Diffusion Model","created":"2023-06-09","cited_by_count":30,"area":"diffusion"},{"id":"2109.10246","title":"Does Vision-and-Language Pretraining Improve Lexical Grounding?","created":"2021-09-21","cited_by_count":25,"area":null},{"id":"2303.07616","title":"The Life Cycle of Knowledge in Big Language Models: A Survey","created":"2023-03-14","cited_by_count":23,"area":null},{"id":"2303.15445","title":"IRFL: Image Recognition of Figurative Language","created":"2023-03-27","cited_by_count":23,"area":null},{"id":"2212.04542","title":"VASR: Visual Analogies of Situation Recognition","created":"2022-12-08","cited_by_count":22,"area":null},{"id":"2207.12576","title":"WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models","created":"2022-07-25","cited_by_count":20,"area":"lr"},{"id":"2303.12513","title":"Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding","created":"2023-03-21","cited_by_count":20,"area":"lr"},{"id":"2109.04947","title":"Tiered Reasoning for Intuitive Physics: Toward Verifiable Commonsense Language Understanding","created":"2021-09-10","cited_by_count":19,"area":null},{"id":"2305.14057","title":"Can Language Models Understand Physical Concepts?","created":"2023-05-23","cited_by_count":19,"area":null},{"id":"2109.07971","title":"Do Language Models Know the Way to Rome?","created":"2021-09-16","cited_by_count":18,"area":null},{"id":"2203.11130","title":"PACS: A Dataset for Physical Audiovisual CommonSense Reasoning","created":"2022-03-21","cited_by_count":18,"area":"lr"},{"id":"2305.09144","title":"Retentive or Forgetful? Diving into the Knowledge Memorizing Mechanism of Language Models","created":"2023-05-16","cited_by_count":18,"area":null},{"id":"2206.01532","title":"Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization","created":"2022-06-03","cited_by_count":17,"area":"lr"},{"id":"2110.15705","title":"Distilling Relation Embeddings from Pre-trained Language Models","created":"2021-09-21","cited_by_count":15,"area":null},{"id":"2210.01963","title":"COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models","created":"2022-10-05","cited_by_count":15,"area":null},{"id":"2407.11015","title":"Does ChatGPT Have a Mind?","created":"2024-06-27","cited_by_count":14,"area":null},{"id":"2310.19559","title":"Disentangled Counterfactual Learning for Physical Audiovisual Commonsense Reasoning","created":"2023-10-30","cited_by_count":13,"area":"lr"},{"id":"2510.24963","title":"Language Model Behavioral Phases are Consistent Across Architecture, Training Data, and Scale","created":"2025-10-28","cited_by_count":13,"area":null},{"id":"2110.06376","title":"ALL Dolphins Are Intelligent and SOME Are Friendly: Probing BERT for Nouns' Semantic Properties and their Prototypicality","created":"2021-10-12","cited_by_count":12,"area":null},{"id":"2106.07285","title":"Probing Pre-Trained Language Models for Disease Knowledge","created":"2021-06-14","cited_by_count":10,"area":null},{"id":"2102.05331","title":"Language Models for Lexical Inference in Context","created":"2021-02-10","cited_by_count":9,"area":null},{"id":"2205.06910","title":"A Property Induction Framework for Neural Language Models","created":"2022-05-13","cited_by_count":9,"area":null},{"id":"2210.02771","title":"Modelling Commonsense Properties using Pre-Trained Bi-Encoders","created":"2022-10-06","cited_by_count":9,"area":null},{"id":"2112.08583","title":"Does Pre-training Induce Systematic Inference? How Masked Language Models Acquire Commonsense Knowledge","created":"2021-12-16","cited_by_count":8,"area":null},{"id":"2305.09785","title":"Distilling Semantic Concept Embeddings from Contrastively Fine-Tuned Language Models","created":"2023-05-16","cited_by_count":8,"area":null},{"id":"2108.09241","title":"Open Relation Modeling: Learning to Define Relations between Entities","created":"2021-08-20","cited_by_count":7,"area":null},{"id":"2109.08270","title":"Language Models as a Knowledge Source for Cognitive Agents","created":"2021-09-17","cited_by_count":7,"area":null},{"id":"2202.09166","title":"Evaluating the Construct Validity of Text Embeddings with Application to Survey Questions","created":"2022-02-18","cited_by_count":7,"area":null},{"id":"2402.14404","title":"On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe","created":"2024-02-22","cited_by_count":6,"area":"lr"},{"id":"2104.10259","title":"Analyzing COVID-19 Tweets with Transformer-based Language Models","created":"2021-04-20","cited_by_count":5,"area":null},{"id":"2204.05673","title":"What do Toothbrushes do in the Kitchen? How Transformers Think our World is Structured","created":"2022-04-12","cited_by_count":5,"area":"lr"},{"id":"2209.07000","title":"VIPHY: Probing \"Visible\" Physical Commonsense Knowledge","created":"2022-09-15","cited_by_count":4,"area":"lr"},{"id":"2306.02258","title":"Probing Physical Reasoning with Counter-Commonsense Context","created":"2023-06-04","cited_by_count":4,"area":"lr"},{"id":"2409.14165","title":"A Survey on Large Language Model-empowered Autonomous Driving","created":"2024-09-21","cited_by_count":4,"area":"driving"},{"id":"2301.11845","title":"Learning the Effects of Physical Actions in a Multi-modal Environment","created":"2023-01-27","cited_by_count":3,"area":null},{"id":"2310.05481","title":"Cabbage Sweeter than Cake? Analysing the Potential of Large Language Models for Learning Conceptual Spaces","created":"2023-10-09","cited_by_count":3,"area":null},{"id":"2311.09308","title":"Divergences between Language Models and Human Brains","created":"2023-11-15","cited_by_count":3,"area":null},{"id":"2503.06980","title":"Exploring Multimodal Perception in Large Language Models Through Perceptual Strength Ratings","created":"2025-03-10","cited_by_count":3,"area":"mllm"},{"id":"2506.03994","title":"Seeing What Tastes Good: Revisiting Multimodal Distributional Semantics in the Billion Parameter Era","created":"2025-06-04","cited_by_count":3,"area":null},{"id":"2106.07947","title":"Deriving Word Vectors from Contextualized Language Models using Topic-Aware Mention Selection","created":"2021-06-15","cited_by_count":2,"area":null},{"id":"2208.11981","title":"On Reality and the Limits of Language Data: Aligning LLMs with Human Norms","created":"2022-08-25","cited_by_count":2,"area":null},{"id":"2209.05286","title":"DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Text","created":"2022-09-12","cited_by_count":2,"area":null},{"id":"2211.01824","title":"Human in the loop approaches in multi-modal conversational task guidance system development","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2409.14065","title":"Temporally Consistent Factuality Probing for Large Language Models","created":"2024-09-21","cited_by_count":2,"area":null},{"id":"2204.07483","title":"Polling Latent Opinions: A Method for Computational Sociolinguistics Using Transformer Language Models","created":"2022-04-15","cited_by_count":1,"area":null},{"id":"2305.14630","title":"Testing Causal Models of Word Meaning in GPT-3 and -4","created":"2023-05-24","cited_by_count":1,"area":null},{"id":"2310.14793","title":"What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies","created":"2023-10-23","cited_by_count":1,"area":null},{"id":"2601.16471","title":"Order from Chaos: Physical World Understanding from Glitchy Gameplay Videos","created":"2026-01-23","cited_by_count":1,"area":null},{"id":"2205.07603","title":"Assessing the Limits of the Distributional Hypothesis in Semantic Spaces: Trait-based Relational Knowledge and the Impact of Co-occurrences","created":"2022-05-16","cited_by_count":0,"area":null},{"id":"2210.17440","title":"Semantic Novelty Detection and Characterization in Factual Text Involving Named Entities","created":"2022-10-31","cited_by_count":0,"area":null},{"id":"2403.16984","title":"Modelling Commonsense Commonalities with Multi-Facet Concept Embeddings","created":"2024-03-25","cited_by_count":0,"area":null},{"id":"2505.22305","title":"IKIWISI: An Interactive Visual Pattern Generator for Evaluating the Reliability of Vision-Language Models Without Ground Truth","created":"2025-05-28","cited_by_count":0,"area":"mllm"},{"id":"2602.07794","title":"Emergent Structured Representations Support Flexible In-Context Inference in Large Language Models","created":"2026-02-08","cited_by_count":0,"area":null},{"id":"2608.25022","title":"A Primer on Computational Semantics for Artificial Intelligence Systems","created":"2026-08-25","cited_by_count":0,"area":null}],"next_cursor":null}