518 citations · 948 across the 34 of their papers we have counts for
38 papers
Guiding Language Models of Code with Global Context using Monitors
Lakshya A Agrawal, Aditya Kanade, Navin Goyal +2
Language models of code (LMs) work well when the surrounding code provides sufficient context. This is not true when it becomes necessary to use types, functionality or APIs define…
In-Context Learning through the Bayesian Prism
Madhur Panwar, Kabir Ahuja, Navin Goyal
In-context learning (ICL) is one of the surprising and useful features of large language models and subject of intense research. Recently, stylized meta-learning-like ICL setups ha…
A Theory of Emergent In-Context Learning as Implicit Structure Induction
Michael Hahn, Navin Goyal
Scaling large language models (LLMs) leads to an emergent capacity to learn in-context from example demonstrations. Despite progress, theoretical understanding of this phenomenon r…
Towards a Mathematics Formalisation Assistant using Large Language Models
Ayush Agrawal, Siddhartha Gadgil, Navin Goyal +2
Mathematics formalisation is the task of writing mathematics (i.e., definitions, theorem statements, proofs) in natural language, as found in books and papers, into a formal langua…
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks
Ankur Sikarwar, Arkil Patel, Navin Goyal
Humans can reason compositionally whilst grounding language utterances to the real world. Recent benchmarks like ReaSCAN use navigation tasks grounded in a grid world to assess whe…
Revisiting the Compositional Generalization Abilities of Neural Sequence Models
Arkil Patel, Satwik Bhattamishra, Phil Blunsom +1
Compositional generalization is a fundamental trait in humans, allowing us to effortlessly combine known phrases to form novel sentences. Recent works have claimed that standard se…