ICLR 2023（投稿）｜自然语言处理相关论文分类整理

  © 作者｜都一凡

  机构｜中国人民大学高瓴人工智能学院 

  研究方向 | 预训练模型 

ICLR是人工智能领域顶级会议之一，会议主题包括深度学习、统计和数据科学，以及一些重要的应用，例如：计算机视觉、计算生物学、语音识别、文本理解、游戏和机器人等。

ICLR 2023将于2023年5月1日至5月5日在卢旺达基加利举行。由于官方的论文接受列表尚未公开，因此本文从投稿论文中选取了与自然语言处理相关的100多篇论文，按照不同的研究主题进行了分类整理，以供参考。

ICLR 2023投稿论文链接如下：https://openreview.net/group?id=ICLR.cc/2023/Conference。

目录

• 模型

• 文本生成

• 机器翻译

• 对话与问答

• 知识与推理

• 多模态

• 信息检索

• 代码

• 数学

• 知识蒸馏

• 表示学习

• 可解释性

• 鲁棒性

• 其他任务

• Benchmark

1. 模型

1.1 模型结构

• EIT: Enhanced Interactive Transformer for Sequence Generation

• Transformers with Multiresolution Attention Heads

• SaMoE: Parameter Efficient MoE Language Models via Self-Adaptive Expert Combination

• Sparse MoE with Random Routing as the New Dropout: Training Bigger and Self-Scalable Models

1.2 模型训练

• Guess the Instruction! Making Language Models Stronger Zero-Shot Learners

• LEXA: Language-agnostic Cross-consistency Training for Question Answering Tasks

• CCT: Cross-consistency training for Clone Detection and Code Search Tasks

• Large Language Models Can Self-improve

• Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning

• PMixUp: Simultaneous Utilization of Part-of-Speech Replacement and Feature Space Interpolation for Text Data Augmentation

• Self-Consistent Learning: Cooperation between Generators and Discriminators

• Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning

• Toward Adversarial Training on Contextualized Language Representation

• ContraGen: Effective Contrastive Learning For Causal Language Model

• Language Model Pre-training with Linguistically Motivated Curriculum Learning

• MLM with Global Co-occurrence

• Improving Language Model Pretraining with Text Structure Information

• Learning by Distilling Context

• MAT: Mixed-Strategy Game of Adversarial Training in Fine-tuning

• Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks

1.3 模型使用

• Prompt Injection: Parameterization of Fixed Inputs

• Meta-Weighted Language Model Tuning for Augmentation-Enhanced Few-Shot Learning

• Pre-trained Language Models can be Fully Zero-Shot Learners

• KnowDA: All-in-One Knowledge Mixture Model for Data Augmentation in Low-Resource NLP

• Contrastive Novelty Learning: Anticipating Outliers with Large Language Models

• Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning

• Mass-Editing Memory in a Transformer

• Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks

• Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models

• Selective Annotation Makes Language Models Better Few-Shot Learners

• Generate rather than Retrieve: Large Language Models are Strong Context Generators

• Ahead-of-Time P-Tuning

• Can discrete information extraction prompts generalize across language models?

2. 文本生成

• Dynamic Scheduled Sampling with Imitation Loss for Neural Text Generation

• DiffusER: Diffusion via Edit-based Reconstruction

• MVP: Multi-task Supervised Pre-training for Natural Language Generation

• Penalizing the High-likelihood: A Novel Sampling Method for Open-ended Neural Text Generation via Inverse Probability Weighting

• RainProof: An Umbrella to Shield Text Generator from Out-Of-Distribution Data

• A Non-monotonic Self-terminating Language Model

• PromptSum: Planning with Mixed Prompts for Parameter-Efficient Controllable Abstractive Summarization

• On the Usefulness of Embeddings, Clusters and Strings for Text Generation Evaluation

• Joint Generator-Ranker Learning for Natural Language Generation

• Calibrating Sequence likelihood Improves Conditional Language Generation

• Sequence to sequence text generation with diffusion models

• Tailoring Language Generation Models under Total Variation Distance

• Language Models Can See: Plugging Visual Controls in Text Generation

• Distribution Aware Metrics for Conditional Natural Language Generation

• PEER: A Collaborative Language Model

3. 机器翻译

• Seq2Seq Pre-training with Dual-channel Recombination for Translation

• Simple and Scalable Nearest Neighbor Machine Translation

• Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation

4. 对话与问答

• Towards Boosting the Open-Domain Chatbot with Human Feedback

• Learning Locality and Isotropy in Dialogue Modeling

• Knowledge-Consistent Dialogue Generation with Language Models and Knowledge Graphs

• Complex-Target-Guided Open-Domain Conversation based on offline reinforcement learning

5. 知识与推理

• ReAct: Synergizing Reasoning and Acting in Language Models

• Language model with Plug-in Knowldge Memory

• Thrust: Adaptively Propels Large Language Models with External Knowledge

• Self-Consistency Improves Chain of Thought Reasoning in Language Models

• DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases

• Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

• Neuro-Symbolic Procedural Planning with Commonsense Prompting

• Multimodal Analogical Reasoning over Knowledge Graphs

• ThinkSum: Probabilistic reasoning over sets using large language models

• Joint Representations of Text and Knowledge Graphs for Retrieval and Evaluation

• Rethinking Identity in Knowledge Graph Embedding

• gGN: learning to represent nodes in directed graphs as low-rank Gaussian distributions

• Don't Throw Your Old Policies Away: Knowledge-based Policy Recycling Protects Against Adversarial Attacks

• Measuring and Narrowing the Compositionality Gap in Language Models

6. 多模态

• CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers

• CLIP model is an Efficient Continual Learner

• Language Modelling with Pixels

• Visual Classification via Description from Large Language Models

• Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning

• RelationCLIP: Training-free Fine-grained Visual and Language Concept Matching

• Contrastive Prompt Tuning Improves Generalization in Vision-Language Models

• Masked Vision and Language Modeling for Multi-modal Representation Learning

• UNIFIED-IO: A Unified Model for Vision, Language, and Multi-modal Tasks

• Visually-augmented pretrained language models for NLP Tasks without Images

• Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings

• VLG: General Video Recognition with Web Textual Knowledge

• Dynamic Historical Adaptation for Continual Image-Text Modeling

• From Images to Textual Prompts: Zero-shot VQA with Frozen Large Language Models

• NÜWA-LIP: Language-guided Image Inpainting with Defect-free VQGAN

• Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval

• Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

• Language-Guided Artistic Style Transfer Using the Latent Space of DALL-E

• Unified Vision and Language Prompt Learning

• DrML: Diagnosing and Rectifying Vision Models using Language

• MaPLe: Multi-modal Prompt Learning

• Prefix Conditioning Unifies Language and Label Supervision

• Domain-Unified Prompt Representations for Source-Free Domain Generalization

• Learning to Decompose Visual Features with Latent Textual Prompts

• Delving into the Openness of CLIP

• Cali-NCE: Boosting Cross-modal Video Representation Learning with Calibrated Alignment

• Dynamic Historical Adaptation for Continual Image-Text Modeling

• Design of the topology for contrastive visual-textual alignment

7. 信息检索

• Multi-Vector Retrieval as Sparse Alignment

• Augmenting Zero-shot Dense Retrievers With Plug-in Mixture-of-memories

• CAMVR: Context-Adaptive Multi-View Representation Learning for Dense Retrieval

8. 代码

• Language Models Can Teach Themselves to Program Better

• Repository-Level Prompt Generation for Large Language Models of Code

• NAPG: Non-Autoregressive Program Generation for Hybrid Tabular-Textual Question Answering

• A Simple, Yet Effective Approach to Finding Biases in Code Generation

• Deep Learning-based Source Code Complexity Prediction

• FixEval: Execution-based Evaluation of Program Fixes for Competitive Programming Problems

• InCoder: A Generative Model for Code Infilling and Synthesis

• Code Translation with Compiler Representations

• CodeT: Code Generation with Generated Tests

• Multi-lingual Evaluation of Code Generation Models

9. 数学

• Learning Math Reasoning from Self-Sampled Correct and Partially-Correct Solutions

• Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

10. 知识蒸馏

• Speed Up Iterative Non-Autoregressive Transformers by Distilling Multiple Steps

• A comparison of dataset distillation and active learning in text classification

• Less is More: Task-aware Layer-wise Distillation for Language Model Compression

• Distilling Text-Image Foundation Models

11. 表示学习

• RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank

• Neural Embeddings for Text

• Ranking-Enhanced Unsupervised Sentence Representation Learning

• Neural Topic Modeling with Embedding Clustering Regularization

• Counterfactual Contrastive Learning for Robust Text Classification

• On The Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations

12. 可解释性

• ORCA: Interpreting Prompted Language Models via Locating Supporting Evidence in the Ocean of Pretraining Data

• ContraSim -- A Similarity Measure Based on Contrastive Learning

13. 鲁棒性

• Learning from Others: Similarity-based Regularization for Mitigating Artifacts

• Randomized Smoothing with Masked Inference for Adversarially Robust NLP Systems

14. 其他任务

• Exploring Methods for Parsing Movie Scripts - Feature Extraction for Further Social Injustice Analysis

• MSQ-BioBERT: Ambiguity Resolution to Enhance BioBERT Medical Question-Answering

• Compositional Semantic Parsing with Large Language Models

• AxBERT: An Explainable Chinese Spelling Correction Method Driven by Associative Knowledge Network

• BED: Boundary-Enhanced Decoder for Chinese Word Segmentation

• Semi-connected Joint Entity Recognition and Relation Extraction of Contextual Entities in Family History Records

15. Benchmark

• GuoFeng: A Discourse-aware Evaluation Benchmark for Language Understanding, Translation and Generation

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