303 citations · 351 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…