165 citations · 455 across the 48 of their papers we have counts for
5 papers · 1 filter
JudgeSense: A Benchmark for Prompt Sensitivity in LLM-as-a-Judge Systems
Rohith Reddy Bellibatlu, Edward Raff, Wenbin Zhang
Large language models are widely adopted as automated evaluation judges, yet the stability of their verdicts under semantically equivalent prompt rephrasings remains largely unexam…
Human-Readable Adversarial Prompts: An Investigation into LLM Vulnerabilities Using Situational Context
Nilanjana Das, Edward Raff, Aman Chadha +1
As the AI systems become deeply embedded in social media platforms, we've uncovered a concerning security vulnerability that goes beyond traditional adversarial attacks. It becomes…
Human-Interpretable Adversarial Prompt Attack on Large Language Models with Situational Context
Nilanjana Das, Edward Raff, Manas Gaur
Previous research on testing the vulnerabilities in Large Language Models (LLMs) using adversarial attacks has primarily focused on nonsensical prompt injections, which are easily…
Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
Deepa Tilwani, Yash Saxena, Seyedali Mohammadi +5
Large language models (LLMs) increasingly generate citation-backed responses, yet citation hallucination remains a major challenge for trustworthy scientific information access. We…
Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling
Stella Biderman, Hailey Schoelkopf, Quentin Anthony +10
How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Py…