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Found 21 papers, 13 papers with code
Calibrating Large Language Models with Sample Consistency
Accurately gauging the confidence level of Large Language Models' (LLMs) predictions is pivotal for their reliable application.
Distilling LLMs' Decomposition Abilities into Compact Language Models
Large Language Models (LLMs) have demonstrated proficiency in their reasoning abilities, yet their large size presents scalability challenges and limits any further customization.
Well begun is half done: Importance of Starting Right in Multi-Step Math Reasoning
We propose two ways in which a smaller model can benefit from initial guidance: 1) asking an LLM for initial guidance, and 2) self-questioning guidance, where the student model can first initiate a question regarding how to start and then continue that chain.
The ART of LLM Refinement: Ask, Refine, and Trust
In recent years, Large Language Models (LLMs) have demonstrated remarkable generative abilities, but can they judge the quality of their own generations?
Ranked #12 on
Arithmetic Reasoning
on GSM8K
SCREWS: A Modular Framework for Reasoning with Revisions
To enable exploration in this space, we present SCREWS, a modular framework for reasoning with revisions.
Distilling Reasoning Capabilities into Smaller Language Models
In this work, we propose an alternative reasoning scheme, Socratic CoT, that learns a decomposition of the original problem into a sequence of subproblems and uses it to guide the intermediate reasoning steps.
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems
On both automatic and human quality evaluations, we find that LMs constrained with desirable question properties generate superior questions and improve the overall performance of a math word problem solver.
A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models
By grounding the behavioral analysis in a causal graph describing an intuitive reasoning process, we study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space.
Longtonotes: OntoNotes with Longer Coreference Chains
Ontonotes has served as the most important benchmark for coreference resolution.
Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs
In principle, applying variational autoencoders (VAEs) to sequential data offers a method for controlled sequence generation, manipulation, and structured representation learning.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
One to rule them all: Towards Joint Indic Language Hate Speech Detection
This paper is a contribution to the Hate Speech and Offensive Content Identification in Indo-European Languages (HASOC) 2021 shared task.
Translational Equivariance in Kernelizable Attention
While Transformer architectures have show remarkable success, they are bound to the computation of all pairwise interactions of input element and thus suffer from limited scalability.
Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages
Language models based on the Transformer architecture have achieved state-of-the-art performance on a wide range of NLP tasks such as text classification, question-answering, and token classification.
End to End Binarized Neural Networks for Text Classification
In this paper, we propose an end to end binarized neural network architecture for the intent classification task.
HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics
The embedding achieved on par F1 scores while decreasing the time and memory requirements by several times compared to the conventional n-gram statistics, e. g., for one of the classifiers on a small dataset, the memory reduction was 6. 18 times; while train and test speed-ups were 4. 62 and 3. 84 times, respectively.
ProbAct: A Probabilistic Activation Function for Deep Neural Networks
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
In this paper, Bayesian Convolutional Neural Network (BayesCNN) using Variational Inference is proposed, that introduces probability distribution over the weights.
Subword Semantic Hashing for Intent Classification on Small Datasets
In this paper, we introduce the use of Semantic Hashing as embedding for the task of Intent Classification and achieve state-of-the-art performance on three frequently used benchmarks.
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference
On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally.
