5 papers
Large Language Models Decide Early and Explain Later
Ayan Datta, Zhixue Zhao, Bhuvanesh Verma +3
Large Language Models often achieve strong performance by generating long intermediate chain-of-thought reasoning. However, it remains unclear when a model's final answer is actual…
From Early Encoding to Late Suppression: Interpreting LLMs on Character Counting Tasks
Ayan Datta, Mounika Marreddy, Alexander Mehler +2
Large language models (LLMs) exhibit failures on elementary symbolic tasks such as character counting in a word, despite excelling on complex benchmarks. Although this limitation h…
Confabulations from ACL Publications (CAP): A Dataset for Scientific Hallucination Detection
Federica Gamba, Aman Sinha, Timothee Mickus +12
We introduce the CAP (Confabulations from ACL Publications) dataset, a multilingual resource for studying hallucinations in large language models (LLMs) within scientific text gene…
Mast Kalandar at SemEval-2024 Task 8: On the Trail of Textual Origins: RoBERTa-BiLSTM Approach to Detect AI-Generated Text
Jainit Sushil Bafna, Hardik Mittal, Suyash Sethia +2
Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse of such te…
Zero-Shot Multi-task Hallucination Detection
Patanjali Bhamidipati, Advaith Malladi, Manish Shrivastava +1
In recent studies, the extensive utilization of large language models has underscored the importance of robust evaluation methodologies for assessing text generation quality and re…