4 citations · 6 across the 4 of their papers we have counts for
4 papers
MALIBU Benchmark: Multi-Agent LLM Implicit Bias Uncovered
Imran Mirza, Cole Huang, Ishwara Vasista +4
Multi-agent systems, which consist of multiple AI models interacting within a shared environment, are increasingly used for persona-based interactions. However, if not carefully de…
Probing Audio-Generation Capabilities of Text-Based Language Models
Arjun Prasaath Anbazhagan, Parteek Kumar, Ujjwal Kaur +3
How does textual representation of audio relate to the Large Language Model's (LLMs) learning about the audio world? This research investigates the extent to which LLMs can be prom…
TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models
Joshua Liu, Aarav Jain, Soham Takuri +5
Rapid improvements in large language models have unveiled a critical challenge in human-AI interaction: sycophancy. In this context, sycophancy refers to the tendency of models to…
ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems
Ishneet Sukhvinder Singh, Ritvik Aggarwal, Ibrahim Allahverdiyev +4
Retrieval-Augmented Generation (RAG) systems using large language models (LLMs) often generate inaccurate responses due to the retrieval of irrelevant or loosely related informatio…