41 citations · 174 across the 34 of their papers we have counts for
9 papers · 1 filter
RESTOR: Knowledge Recovery in Machine Unlearning
Keivan Rezaei, Khyathi Chandu, Soheil Feizi +3
Large language models trained on web-scale corpora can memorize undesirable data containing misinformation, copyrighted material, or private or sensitive information. Recently, sev…
Reverse Question Answering: Can an LLM Write a Question so Hard (or Bad) that it Can't Answer?
Nishant Balepur, Feng Gu, Abhilasha Ravichander +3
Question answering (QA), giving correct answers to questions, is a popular task, but we test reverse question answering (RQA): for an input answer, give a question with that answer…
WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries
Wenting Zhao, Tanya Goyal, Yu Ying Chiu +8
While hallucinations of large language models (LLMs) prevail as a major challenge, existing evaluation benchmarks on factuality do not cover the diverse domains of knowledge that t…
The Art of Saying No: Contextual Noncompliance in Language Models
Faeze Brahman, Sachin Kumar, Vidhisha Balachandran +11
Chat-based language models are designed to be helpful, yet they should not comply with every user request. While most existing work primarily focuses on refusal of "unsafe" queries…
WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild
Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu +6
We introduce WildBench, an automated evaluation framework designed to benchmark large language models (LLMs) using challenging, real-world user queries. WildBench consists of 1,024…
BEAR: A Unified Framework for Evaluating Relational Knowledge in Causal and Masked Language Models
Jacek Wiland, Max Ploner, Alan Akbik
Knowledge probing assesses to which degree a language model (LM) has successfully learned relational knowledge during pre-training. Probing is an inexpensive way to compare LMs of…
Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?
Nishant Balepur, Abhilasha Ravichander, Rachel Rudinger
Multiple-choice question answering (MCQA) is often used to evaluate large language models (LLMs). To see if MCQA assesses LLMs as intended, we probe if LLMs can perform MCQA with c…
OLMo: Accelerating the Science of Language Models
Dirk Groeneveld, Iz Beltagy, Pete Walsh +40
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have beco…
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini, Rodney Kinney, Akshita Bhagia +33
Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open model…