TL;DR
LiteLLM is introduced, a lightweight transformer architecture explicitly optimized for short-text scenarios that delivers competitive performance, fast convergence, and competitive cross-domain performance across heterogeneous short-text settings.
This paper presents a context-aware hybrid deep learning approach by integrating the Robustly Optimized BERT Pretraining Approach (RoBERTa) with Bidirectional Long Short-Term Memory (BiLSTM) networks to generate rich contextual word embeddings.
This study proposes a hybrid deep-learning framework that combines convolutional feature extraction with a bidirectional recurrent encoder and an attention mechanism to categories tweets as positive, neutral, or negative, and indicates that the learned representations generalize reasonably well across domains rather th...
Empirical evidence is shown indicating that composing several imbalance-oriented techniques on a pretrained transformer can yield adverse interactions rather than cumulative gains.
The work provides a reproducible, explainable, operationally applicable model of sentiment analysis in operationally sensitive, high-stakes Twitter sentiment analysis, and validate the hypothesis that hybrid stacking is an effective method for leveraging the complementary nature of lexical and contextual representation...
This work evaluates a fixed-list routing strategy that keeps stronger languages on a direct multilingual path and selectively sends weaker languages through translation into English before zero-shot classification, and reports routing through tier-level quality gains and tier-level latency rather than a single global e...
A hybrid detection framework which combines semantically deep embeddings from the RoBERTa transformer with a set of carefully designed language statistics and linguistic statistics and shows excellent resistance to the surface-level adversarial paraphrasing strategy.
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