no code implementations • 27 Oct 2023 • Nitish Joshi, Javier Rando, Abulhair Saparov, Najoung Kim, He He
This allows the model to separate truth from falsehoods and controls the truthfulness of its generation.
1 code implementation • NeurIPS 2023 • Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, He He
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity.
1 code implementation • 22 May 2023 • Chenglei Si, Dan Friedman, Nitish Joshi, Shi Feng, Danqi Chen, He He
We investigate the inductive biases of ICL from the perspective of feature bias: which feature ICL is more likely to use given a set of underspecified demonstrations in which two features are equally predictive of the labels.
1 code implementation • 25 Oct 2022 • Nitish Joshi, Xiang Pan, He He
In case (i), we want the model to be invariant to the feature, which is neither necessary nor sufficient for prediction.
no code implementations • 4 Oct 2022 • Aahlad Puli, Nitish Joshi, He He, Rajesh Ranganath
In prediction tasks, there exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics.
2 code implementations • NAACL 2022 • Richard Yuanzhe Pang, Alicia Parrish, Nitish Joshi, Nikita Nangia, Jason Phang, Angelica Chen, Vishakh Padmakumar, Johnny Ma, Jana Thompson, He He, Samuel R. Bowman
To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5, 000 tokens, much longer than typical current models can process.
1 code implementation • ACL 2022 • Nitish Joshi, He He
While pretrained language models achieve excellent performance on natural language understanding benchmarks, they tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data.
1 code implementation • 14 May 2020 • Vinit Unni, Nitish Joshi, Preethi Jyothi
We propose coupled training for encoder-decoder ASR models that acts on pairs of utterances corresponding to the same text spoken by speakers with different accents.
1 code implementation • ACL 2019 • Yichen Jiang, Nitish Joshi, Yen-Chun Chen, Mohit Bansal
Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context.
1 code implementation • ACL 2019 • Vishwajeet Kumar, Nitish Joshi, Arijit Mukherjee, Ganesh Ramakrishnan, Preethi Jyothi
For a new language, such training instances are hard to obtain making the QG problem even more challenging.