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Found 12 papers, 7 papers with code
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations
Additionally, our analysis uncovers the semantic predispositions in LLMs and reveals the impact of recency bias for information presented in long contexts.
Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions
In this work, we take a step towards answering these questions by demonstrating the following: (a) On a test-bed with a variety of Boolean function classes, we find that Transformers can nearly match the optimal learning algorithm for 'simpler' tasks, while their performance deteriorates on more 'complex' tasks.
Structural Transfer Learning in NL-to-Bash Semantic Parsers
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.
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization
With the increase in the scale of Deep Learning (DL) training workloads in terms of compute resources and time consumption, the likelihood of encountering in-training failures rises substantially, leading to lost work and resource wastage.
Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions
(ii) When trained on Boolean functions, both Transformers and LSTMs prioritize learning functions of low sensitivity, with Transformers ultimately converging to functions of lower sensitivity.
Revisiting the Compositional Generalization Abilities of Neural Sequence Models
Compositional generalization is a fundamental trait in humans, allowing us to effortlessly combine known phrases to form novel sentences.
Are NLP Models really able to Solve Simple Math Word Problems?
Since existing solvers achieve high performance on the benchmark datasets for elementary level MWPs containing one-unknown arithmetic word problems, such problems are often considered "solved" with the bulk of research attention moving to more complex MWPs.
Ranked #1 on
Math Word Problem SolvingΩ
on MAWPS
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages
We find that while recurrent models generalize nearly perfectly if the lengths of the training and test strings are from the same range, they perform poorly if the test strings are longer.
On the Ability and Limitations of Transformers to Recognize Formal Languages
Our analysis also provides insights on the role of self-attention mechanism in modeling certain behaviors and the influence of positional encoding schemes on the learning and generalization abilities of the model.
On the Computational Power of Transformers and its Implications in Sequence Modeling
Transformers are being used extensively across several sequence modeling tasks.
Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities
In this paper, we examine and analyze the challenges associated with developing and introducing language technologies to low-resource language communities.
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation
Inducing diversity in the task of paraphrasing is an important problem in NLP with applications in data augmentation and conversational agents.
