3 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Limitations on Accurate, Trusted, Human-level Reasoning
Rina Panigrahy, Vatsal Sharan
We identify a fundamental incompatibility between the goals of accuracy, trust, and human-level reasoning in artificial intelligence (AI) systems, for strict mathematical definitio…
Latent Concept Disentanglement in Transformer-based Language Models
Guan Zhe Hong, Bhavya Vasudeva, Vatsal Sharan +3
When large language models (LLMs) use in-context learning (ICL) to solve a new task, they must infer latent concepts from demonstration examples. This raises the question of whethe…
StagFormer: Time Staggering Transformer Decoding for RunningLayers In Parallel
Dylan Cutler, Arun Kandoor, Nishanth Dikkala +3
Decoding in a Transformer based language model is inherently sequential as a token's embedding needs to pass through all the layers in the network before the generation of the next…
A Implies B: Circuit Analysis in LLMs for Propositional Logical Reasoning
Guan Zhe Hong, Nishanth Dikkala, Enming Luo +3
Due to the size and complexity of modern large language models (LLMs), it has proven challenging to uncover the underlying mechanisms that models use to solve reasoning problems. F…
Causal Language Modeling Can Elicit Search and Reasoning Capabilities on Logic Puzzles
Kulin Shah, Nishanth Dikkala, Xin Wang +1
Causal language modeling using the Transformer architecture has yielded remarkable capabilities in Large Language Models (LLMs) over the last few years. However, the extent to whic…
Simple Mechanisms for Representing, Indexing and Manipulating Concepts
Yuanzhi Li, Raghu Meka, Rina Panigrahy +1
Supervised and unsupervised learning using deep neural networks typically aims to exploit the underlying structure in the training data; this structure is often explained using a l…