2 citations · 16 across the 47 of their papers we have counts for
5 papers · 1 filter
BiasMix-Finance: Post-Generation KYC Guardrails for LLM Portfolio Advice
Gaurav Kukreja, Parul Kukreja, Mohammed Abraar +3
Large language models (LLMs) can generate plausible-sounding ETF portfolios while silently violating basic KYC-style constraints on risk, fees, and diversification. This is especia…
Three methods, one problem: Classical and AI approaches to no-three-in-line
Pranav Ramanathan, Thomas Prellberg, Matthew Lewis +4
The No-Three-In-Line problem asks for the maximum number of points that can be placed on an n by n grid with no three collinear, representing a famous problem in combinatorial geom…
Unifying Mixture of Experts and Multi-Head Latent Attention for Efficient Language Models
Sushant Mehta, Raj Dandekar, Rajat Dandekar +1
We present MoE-MLA-RoPE, a novel architecture combination that combines Mixture of Experts (MoE) with Multi-head Latent Attention (MLA) and Rotary Position Embeddings (RoPE) for ef…
Model-Grounded Symbolic Artificial Intelligence Systems Learning and Reasoning with Model-Grounded Symbolic Artificial Intelligence Systems
Aniruddha Chattopadhyay, Raj Dandekar, Kaushik Roy
Neurosymbolic artificial intelligence (AI) systems combine neural network and classical symbolic AI mechanisms to exploit the complementary strengths of large scale, generalizable…
HULLMI: Human vs LLM identification with explainability
Prathamesh Dinesh Joshi, Sahil Pocker, Raj Abhijit Dandekar +2
As LLMs become increasingly proficient at producing human-like responses, there has been a rise of academic and industrial pursuits dedicated to flagging a given piece of text as "…