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Found 7 papers, 5 papers with code
Offline Training of Language Model Agents with Functions as Learnable Weights
Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions.
Better Explain Transformers by Illuminating Important Information
Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings.
NLPBench: Evaluating Large Language Models on Solving NLP Problems
Recent developments in large language models (LLMs) have shown promise in enhancing the capabilities of natural language processing (NLP).
SCP: Spherical-Coordinate-based Learned Point Cloud Compression
In recent years, the task of learned point cloud compression has gained prominence.
Taming Small-sample Bias in Low-budget Active Learning
Instead of tuning the coefficient for each query round, which is sensitive and time-consuming, we propose the curriculum Firth bias reduction (CHAIN) that can automatically adjust the coefficient to be adaptive to the training process.
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision
In particular, it is built on a mixture of Bayesian label models, each corresponding to a global pattern of correlation, and the coefficients of the mixture components are predicted by a Gaussian Process classifier based on instance features.
Adaptive Ranking-based Sample Selection for Weakly Supervised Class-imbalanced Text Classification
To obtain a large amount of training labels inexpensively, researchers have recently adopted the weak supervision (WS) paradigm, which leverages labeling rules to synthesize training labels rather than using individual annotations to achieve competitive results for natural language processing (NLP) tasks.
