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Found 10 papers, 2 papers with code
FRACTAL: Fine-Grained Scoring from Aggregate Text Labels
Large language models (LLMs) are being increasingly tuned to power complex generation tasks such as writing, fact-seeking, querying and reasoning.
LLP-Bench: A Large Scale Tabular Benchmark for Learning from Label Proportions
One of the unique properties of tabular LLP is the ability to create feature bags where all the instances in a bag have the same value for a given feature.
Label Differential Privacy via Aggregation
Further, the $\ell_2^2$-regressor which minimizes the loss on the aggregated dataset has a loss within $\left(1 + o(1)\right)$-factor of the optimum on the original dataset w. p.
Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
We then use Belief Propagation (BP) to marginalize the Gibbs distribution to obtain pseudo labels.
Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
Our third contribution is theoretical, where we show that our weighted entropy term along with prediction loss on the training set approximates test loss under covariate shift.
BiPhone: Modeling Inter Language Phonetic Influences in Text
To the best of our knowledge, FunGLUE is the first benchmark to introduce L1-L2 interactions in text.
ReTAG: Reasoning Aware Table to Analytic Text Generation
The task of table summarization involves generating text that both succinctly and accurately represents the table or a specific set of highlighted cells within a table.
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
In this work, we study the usefulness of Abstract Meaning Representation (AMR) graph as the intermediate style agnostic representation.
Multi-Variate Time Series Forecasting on Variable Subsets
Through systematic experiments across 4 datasets and 5 forecast models, we show that our technique is able to recover close to 95\% performance of the models even when only 15\% of the original variables are present.
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints
For such cases, we propose training the model with additional hints (as target tags on the decoder) that provide information about the operation required on the source (translation or both translation and transliteration).
