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Found 29 papers, 12 papers with code
Negating Negatives: Alignment without Human Positive Samples via Distributional Dispreference Optimization
Large language models (LLMs) have revolutionized the role of AI, yet also pose potential risks of propagating unethical content.
A Survey on Evaluation of Large Language Models
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
Chinese Poetry Generation with a Working Memory Model
Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory.
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills.
Ranked #1 on
Grammatical Error Detection
on FCE
Self-explaining deep models with logic rule reasoning
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision.
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation
The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks.
CCPM: A Chinese Classical Poetry Matching Dataset
We hope this dataset can further enhance the study on incorporating deep semantics into the understanding and generation system of Chinese classical poetry.
CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models
This benchmark serves as a valuable resource for cultural studies in LLMs, paving the way for more culturally aware and sensitive models.
ToViLaG: Your Visual-Language Generative Model is Also An Evildoer
Warning: this paper includes model outputs showing offensive content.
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention
In this work, we dig into the intrinsic mechanism of this problem and found that sparser attention values in Transformer could improve diversity.
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation
Augmented by only self-generated pseudo text, generation models over-emphasize exploitation of the previously learned space, suffering from a constrained generalization boundary.
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation
Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning.
Generating Chinese Classical Poems with RNN Encoder-Decoder
We take the generation of Chinese classical poem lines as a sequence-to-sequence learning problem, and build a novel system based on the RNN Encoder-Decoder structure to generate quatrains (Jueju in Chinese), with a topic word as input.
Chinese Poetry Generation with a Salient-Clue Mechanism
As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations.
Automatic Poetry Generation with Mutual Reinforcement Learning
Human experts evaluate poetry in terms of some specific criteria, instead of word-level likelihood.
Stylistic Chinese Poetry Generation via Unsupervised Style Disentanglement
The ability to write diverse poems in different styles under the same poetic imagery is an important characteristic of human poetry writing.
Jiuge: A Human-Machine Collaborative Chinese Classical Poetry Generation System
By exposing the options of poetry genres, styles and revision modes, Jiuge, acting as a professional assistant, allows constant and active participation of users in poetic creation.
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space
Though recent neural models make prominent progress in some criteria of poetry quality, generated poems still suffer from the problem of poor diversity.
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Warning: this paper contains model outputs exhibiting offensiveness and biases.
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation
We demonstrate that TRACE could enhance the entanglement of each segment and preceding latent variables and deduce a non-zero lower bound of the KL term, providing a theoretical guarantee of generation diversity.
Efficient Cross-Lingual Transfer for Chinese Stable Diffusion with Images as Pivots
IAP optimizes only a separate Chinese text encoder with all other parameters fixed to align Chinese semantics space to the English one in CLIP.
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly improved performance across diverse tasks but also present emergent capabilities absent in smaller models.
Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning
We discovered that most models are essentially misaligned, necessitating further ethical value alignment.
Unpacking the Ethical Value Alignment in Big Models
Big models have greatly advanced AI's ability to understand, generate, and manipulate information and content, enabling numerous applications.
Knowledge Plugins: Enhancing Large Language Models for Domain-Specific Recommendations
In this paper, we propose a general paradigm that augments LLMs with DOmain-specific KnowledgE to enhance their performance on practical applications, namely DOKE.
Value FULCRA: Mapping Large Language Models to the Multidimensional Spectrum of Basic Human Values
The rapid advancement of Large Language Models (LLMs) has attracted much attention to value alignment for their responsible development.
LegalDuet: Learning Effective Representations for Legal Judgment Prediction through a Dual-View Legal Clue Reasoning
It proposes a dual-view legal clue reasoning mechanism, which derives from two reasoning chains of judges: 1) Law Case Reasoning, which makes legal judgments according to the judgment experiences learned from analogy/confusing legal cases; 2) Legal Ground Reasoning, which lies in matching the legal clues between criminal cases and legal decisions.
ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph
While achieving remarkable progress in a broad range of tasks, large language models (LLMs) remain significantly limited in properly using massive external tools.
On the Essence and Prospect: An Investigation of Alignment Approaches for Big Models
Big models have achieved revolutionary breakthroughs in the field of AI, but they might also pose potential concerns.
