• NLPapers
• Why Does ChatGPT Fall Short in Providing Truthful Answers?
• Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization
• AgentBench: Evaluating LLMs as Agents
• Survey of Hallucination in Natural Language Generation
• A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
• The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations
• G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment
• Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension
• WikiQA: A Challenge Dataset for Open-Domain Question Answering
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• Robust MT Evaluation with Sentence-level Multilingual Augmentation
• How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
• Evaluating LLMs at Detecting Errors in LLM Responses
• GAIA: a benchmark for General AI Assistants
• Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
• Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization
• Deep Learning in Spiking Neural Networks