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Found 22 papers, 11 papers with code
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation
Emotion recognition in conversation has received considerable attention recently because of its practical industrial applications.
Ranked #40 on
Emotion Recognition in Conversation
on IEMOCAP
Predicate-Argument Based Bi-Encoder for Paraphrase Identification
Paraphrase identification involves identifying whether a pair of sentences express the same or similar meanings.
Counter-Contrastive Learning for Language GANs
Generative Adversarial Networks (GANs) have achieved great success in image synthesis, but have proven to be difficult to generate natural language.
Curiosity-Driven Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but often at the cost of reduced output diversity.
MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions
However, token-level RLHF suffers from the credit assignment problem over long sequences, where delayed rewards make it challenging for the model to discern which actions contributed to successful outcomes.
Tokenization Falling Short: On Subword Robustness in Large Language Models
Our findings reveal that scaling model parameters can mitigate the issue of tokenization; however, LLMs still suffer from biases induced by typos and other text format variations.
Autoregressive Pre-Training on Pixels and Texts
Our extensive evaluation across a wide range of benchmarks shows that incorporating both visual and textual data significantly improves the performance of pixel-based language models.
On Training Data Influence of GPT Models
This paper presents GPTfluence, a novel approach that leverages a featurized simulation to assess the impact of training examples on the training dynamics of GPT models.
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
Despite these efforts, such models encounter challenges such as limited multilingual capabilities, risks of catastrophic forgetting during continual pretraining, and the high costs of training models from scratch, alongside the need to align with AI safety standards and regulatory frameworks.
StarCoder 2 and The Stack v2: The Next Generation
Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size.
Ranked #35 on
Code Generation
on MBPP
HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization
These benchmarks have overlooked the vast landscape of massively multilingual NL to multilingual code, leaving a critical gap in the evaluation of multilingual LLMs.
Tool-Augmented Reward Modeling
We validate our approach across a wide range of domains, incorporating seven distinct external tools.
Improved Training of Mixture-of-Experts Language GANs
In this work, we (1) first empirically show that the mixture-of-experts approach is able to enhance the representation capacity of the generator for language GANs and (2) harness the Feature Statistics Alignment (FSA) paradigm to render fine-grained learning signals to advance the generator training.
ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models
In response to this lacuna, this paper introduces a pioneering contribution in the form of a text-to-waveform music generation model, underpinned by the utilization of diffusion models.
ERNIE-Code: Beyond English-Centric Cross-lingual Pretraining for Programming Languages
Extensive results show that ERNIE-Code outperforms previous multilingual LLMs for PL or NL across a wide range of end tasks of code intelligence, including multilingual code-to-text, text-to-code, code-to-code, and text-to-text generation.
X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection
Detecting sarcasm and verbal irony from people's subjective statements is crucial to understanding their intended meanings and real sentiments and positions in social scenarios.
Clip-Tuning: Towards Derivative-free Prompt Learning with a Mixture of Rewards
Derivative-free prompt learning has emerged as a lightweight alternative to prompt tuning, which only requires model inference to optimize the prompts.
RefineCap: Concept-Aware Refinement for Image Captioning
Automatically translating images to texts involves image scene understanding and language modeling.
Ranked #27 on
Image Captioning
on COCO Captions
Improving Sequence Generative Adversarial Networks with Feature Statistics Alignment
Generative Adversarial Networks (GAN) are facing great challenges in synthesizing sequences of discrete elements, such as mode dropping and unstable training.
Neural Text Classification by Jointly Learning to Cluster and Align
Distributional text clustering delivers semantically informative representations and captures the relevance between each word and semantic clustering centroids.
Highway Transformer: Self-Gating Enhanced Self-Attentive Networks
Self-attention mechanisms have made striking state-of-the-art (SOTA) progress in various sequence learning tasks, standing on the multi-headed dot product attention by attending to all the global contexts at different locations.
How to Evaluate Word Representations of Informal Domain?
Diverse word representations have surged in most state-of-the-art natural language processing (NLP) applications.
