14 citations · 16 across the 13 of their papers we have counts for
7 papers · 1 filter
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
Arkil Patel, Satwik Bhattamishra, Siva Reddy +1
Humans possess a remarkable ability to assign novel interpretations to linguistic expressions, enabling them to learn new words and understand community-specific connotations. Howe…
Structural Transfer Learning in NL-to-Bash Semantic Parsers
Kyle Duffy, Satwik Bhattamishra, Phil Blunsom
Large-scale pre-training has made progress in many fields of natural language processing, though little is understood about the design of pre-training datasets. We propose a method…
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…
Are NLP Models really able to Solve Simple Math Word Problems?
Arkil Patel, Satwik Bhattamishra, Navin Goyal
The problem of designing NLP solvers for math word problems (MWP) has seen sustained research activity and steady gains in the test accuracy. Since existing solvers achieve high pe…
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages
Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
While recurrent models have been effective in NLP tasks, their performance on context-free languages (CFLs) has been found to be quite weak. Given that CFLs are believed to capture…
On the Ability and Limitations of Transformers to Recognize Formal Languages
Satwik Bhattamishra, Kabir Ahuja, Navin Goyal
Transformers have supplanted recurrent models in a large number of NLP tasks. However, the differences in their abilities to model different syntactic properties remain largely unk…