25 citations · 83 across the 27 of their papers we have counts for
10 papers · 1 filter
GEO: Generative Engine Optimization
Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit +3
The advent of large language models (LLMs) has ushered in a new paradigm of search engines that use generative models to gather and summarize information to answer user queries. Th…
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande +4
Recent works have showcased the ability of LLMs to embody diverse personas in their responses, exemplified by prompts like 'You are Yoda. Explain the Theory of Relativity.' While t…
QualEval: Qualitative Evaluation for Model Improvement
Vishvak Murahari, Ameet Deshpande, Peter Clark +4
Quantitative evaluation metrics have traditionally been pivotal in gauging the advancements of artificial intelligence systems, including large language models (LLMs). However, the…
Distraction-free Embeddings for Robust VQA
Atharvan Dogra, Deeksha Varshney, Ashwin Kalyan +2
The generation of effective latent representations and their subsequent refinement to incorporate precise information is an essential prerequisite for Vision-Language Understanding…
InstructEval: Systematic Evaluation of Instruction Selection Methods
Anirudh Ajith, Chris Pan, Mengzhou Xia +2
In-context learning (ICL) performs tasks by prompting a large language model (LLM) using an instruction and a small set of annotated examples called demonstrations. Recent work has…
Anthropomorphization of AI: Opportunities and Risks
Ameet Deshpande, Tanmay Rajpurohit, Karthik Narasimhan +1
Anthropomorphization is the tendency to attribute human-like traits to non-human entities. It is prevalent in many social contexts -- children anthropomorphize toys, adults do so w…