Large Language Models

Walking in Others’ Shoes: How Perspective-Taking Guides Large Language Models in Reducing Toxicity and Bias EMNLP 2024 WARM: On the Benefits of Weight Averaged Reward Models ICML 2024 WaterBench: Towards Holistic Evaluation of Watermarks for Large Language Models ACL 2024 Watermarks in the Sand: Impossibility of Strong Watermarking for Language Models ICML 2024 Watermark Stealing in Large Language Models ICML 2024 Weak-eval-Strong: Evaluating and Eliciting Lateral Thinking of LLMs with Situation Puzzles NeurIPS 2024 Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision ICML 2024 Weak-to-Strong Reasoning EMNLP 2024 Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models NeurIPS 2024 Weighted Layer Averaging RoBERTa for Black-Box Machine-Generated Text Detection SemEval 2024 WenMind: A Comprehensive Benchmark for Evaluating Large Language Models in Chinese Classical Literature and Language Arts NeurIPS 2024 What Are the Odds? Language Models Are Capable of Probabilistic Reasoning EMNLP 2024 What Does Parameter-free Probing Really Uncover? ACL 2024 What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection ACL 2024 What Do Language Models Hear? Probing for Auditory Representations in Language Models ACL 2024 What Do Language Models Learn in Context? The Structured Task Hypothesis. ACL 2024 What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models EMNLP 2024 What Kind of Sourcery is This? Evaluating GPT-4’s Performance on Linking Scientific Fact to Citations EMNLP 2024 What Makes and Breaks Safety Fine-tuning? A Mechanistic Study NeurIPS 2024 What matters when building vision-language models? NeurIPS 2024 When ”A Helpful Assistant” Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models EMNLP 2024 When Context Leads but Parametric Memory Follows in Large Language Models EMNLP 2024 When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets EACL 2024 When Do LLMs Need Retrieval Augmentation? Mitigating LLMs’ Overconfidence Helps Retrieval Augmentation ACL 2024 When is a Metaphor Actually Novel? Annotating Metaphor Novelty in the Context of Automatic Metaphor Detection EACL 2024