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Found 59 papers, 27 papers with code
Improving Controllable Text Generation with Position-Aware Weighted Decoding
Weighted decoding methods composed of the pretrained language model (LM) and the controller have achieved promising results for controllable text generation.
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization
Dialogue summarization helps users capture salient information from various types of dialogues has received much attention recently.
SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment
Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning.
Ensuring Consistency for In-Image Translation
Subsequently, a diffusion model trained for style-consistent text-image generation ensures uniformity in text style within images and preserves background details.
Cross-Lingual Text-Rich Visual Comprehension: An Information Theory Perspective
To mitigate this issue, we propose MVCL-MI (Maximization of Vision-Language Cross-Lingual Mutual Information), where a visual-text cross-lingual alignment is built by maximizing mutual information between the model's outputs and visual information.
Length Controlled Generation for Black-box LLMs
Large language models (LLMs) have demonstrated impressive instruction following capabilities, while still struggling to accurately manage the length of the generated text, which is a fundamental requirement in many real-world applications.
CoMT: A Novel Benchmark for Chain of Multi-modal Thought on Large Vision-Language Models
Large Vision-Language Models (LVLMs) have recently demonstrated amazing success in multi-modal tasks, including advancements in Multi-modal Chain-of-Thought (MCoT) reasoning.
XTransplant: A Probe into the Upper Bound Performance of Multilingual Capability and Culture Adaptability in LLMs via Mutual Cross-lingual Feed-forward Transplantation
Through extensive pilot experiments, we empirically prove that both the multilingual capabilities and cultural adaptability of LLMs hold the potential to be significantly improved by XTransplant, respectively from En -> non-En and non-En -> En, highlighting the underutilization of current LLMs' multilingual potential.
Discrete Modeling via Boundary Conditional Diffusion Processes
Experimental results indicate that our approach achieves strong performance in both language modeling and discrete image generation tasks.
Advancing Large Language Model Attribution through Self-Improving
Teaching large language models (LLMs) to generate text with citations to evidence sources can mitigate hallucinations and enhance verifiability in information-seeking systems.
GlobeSumm: A Challenging Benchmark Towards Unifying Multi-lingual, Cross-lingual and Multi-document News Summarization
News summarization in today's global scene can be daunting with its flood of multilingual content and varied viewpoints from different sources.
Extending Context Window of Large Language Models from a Distributional Perspective
However, existing scaling methods often rely on empirical approaches and lack a profound understanding of the internal distribution within RoPE, resulting in suboptimal performance in extending the context window length.
Learning Fine-Grained Grounded Citations for Attributed Large Language Models
Despite the impressive performance on information-seeking tasks, large language models (LLMs) still struggle with hallucinations.
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models
To mitigate this, we further propose a training-free method called Residual Visual Decoding, where we revise the output distribution of LVLMs with the one derived from the residual visual input, providing models with direct access to the visual information.
Unveiling Entity-Level Unlearning for Large Language Models: A Comprehensive Analysis
Large language model unlearning has garnered increasing attention due to its potential to address security and privacy concerns, leading to extensive research in the field.
An Information Bottleneck Perspective for Effective Noise Filtering on Retrieval-Augmented Generation
Retrieval-augmented generation integrates the capabilities of large language models with relevant information retrieved from an extensive corpus, yet encounters challenges when confronted with real-world noisy data.
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models.
Ranked #1 on
Visual Question Answering
on AMBER
Relay Decoding: Concatenating Large Language Models for Machine Translation
Leveraging large language models for machine translation has demonstrated promising results.
Ensemble Learning for Heterogeneous Large Language Models with Deep Parallel Collaboration
To address this challenge, DeePEn maps the probability distribution of each model from its own probability space to a universal relative space based on the relative representation theory, and performs aggregation.
Aligning Translation-Specific Understanding to General Understanding in Large Language Models
Large Language models (LLMs) have exhibited remarkable abilities in understanding complex texts, offering a promising path towards human-like translation performance.
Length Extrapolation of Transformers: A Survey from the Perspective of Positional Encoding
Built upon the Transformer, large language models (LLMs) have captured worldwide attention due to their remarkable abilities.
Emage: Non-Autoregressive Text-to-Image Generation
Compared with autoregressive baselines that needs to run one thousand times, our model only runs 16 times to generate images of competitive quality with an order of magnitude lower inference latency.
Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications
In this paper, we propose a review to discuss the trends in integration of knowledge and large language models, including taxonomy of methods, benchmarks, and applications.
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
This divergence highlights the urgency for a nuanced understanding and comprehensive overview of recent advances in LLM hallucinations.
Retrieval-Generation Synergy Augmented Large Language Models
Large language models augmented with task-relevant documents have demonstrated impressive performance on knowledge-intensive tasks.
Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization
However, existing work has focused on training models on centralized data, neglecting real-world scenarios where meeting data are infeasible to collect centrally, due to their sensitive nature.
SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills
Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge.
Improved Visual Story Generation with Adaptive Context Modeling
Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation.
Towards Higher Pareto Frontier in Multilingual Machine Translation
Multilingual neural machine translation has witnessed remarkable progress in recent years.
The Role of Summarization in Generative Agents: A Preliminary Perspective
Generative agents that simulate human society show tremendous potential for further research and practical applications.
Hierarchical Catalogue Generation for Literature Review: A Benchmark
Scientific literature review generation aims to extract and organize important information from an abundant collection of reference papers and produces corresponding reviews while lacking a clear and logical hierarchy.
STOA-VLP: Spatial-Temporal Modeling of Object and Action for Video-Language Pre-training
Although large-scale video-language pre-training models, which usually build a global alignment between the video and the text, have achieved remarkable progress on various downstream tasks, the idea of adopting fine-grained information during the pre-training stage is not well explored.
Semantic-aware Contrastive Learning for Electroencephalography-to-Text Generation with Curriculum Learning
To mitigate this challenge, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C-SCL), which effectively re-calibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thus reducing the discrepancy.
Controllable Text Generation via Probability Density Estimation in the Latent Space
Previous work on controllable text generation has explored the idea of control from the latent space, such as optimizing a representation with attribute-related classifiers or sampling a representation from relevant discrete samples.
Unifying the Convergences in Multilingual Neural Machine Translation
In this paper, we propose a novel training strategy named LSSD (Language-Specific Self-Distillation), which can alleviate the convergence inconsistency and help MNMT models achieve the best performance on each language pair simultaneously.
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words
(2) how to predict a word via cloze test without knowing the number of wordpieces in advance?
A Survey on Dialogue Summarization: Recent Advances and New Frontiers
We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.
Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization
Current dialogue summarization systems usually encode the text with a number of general semantic features (e. g., keywords and topics) to gain more powerful dialogue modeling capabilities.
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text.
Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization
First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations.
TableGPT: Few-shot Table-to-Text Generation with Table Structure Reconstruction and Content Matching
Although neural table-to-text models have achieved remarkable progress with the help of large-scale datasets, they suffer insufficient learning problem with limited training data.
Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification
Neural table-to-text models, which select and order salient data, as well as verbalizing them fluently via surface realization, have achieved promising progress.
Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks
In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information.
Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation
In this paper, we focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer and aims to preserve text styles while altering the content.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
Ranked #1 on
Code Documentation Generation
on CodeSearchNet - Go
Enhancing Neural Data-To-Text Generation Models with External Background Knowledge
Recent neural models for data-to-text generation rely on massive parallel pairs of data and text to learn the writing knowledge.
Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning
Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data.
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)
To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table's representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively.
Adaptive Multi-pass Decoder for Neural Machine Translation
Although end-to-end neural machine translation (NMT) has achieved remarkable progress in the recent years, the idea of adopting multi-pass decoding mechanism into conventional NMT is not well explored.
Knowledge Based Machine Reading Comprehension
Machine reading comprehension (MRC) requires reasoning about both the knowledge involved in a document and knowledge about the world.
Knowledge-Aware Conversational Semantic Parsing Over Web Tables
Results show that our knowledge-aware model outperforms the state-of-the-art approaches.
Semantic Parsing with Syntax- and Table-Aware SQL Generation
We present a generative model to map natural language questions into SQL queries.
Ranked #4 on
Code Generation
on WikiSQL
English-Chinese Knowledge Base Translation with Neural Network
Knowledge base (KB) such as Freebase plays an important role for many natural language processing tasks.
Bitext Name Tagging for Cross-lingual Entity Annotation Projection
Instead of directly relying on word alignment results, this framework combines advantages of rule-based methods and deep learning methods by implementing two steps: First, generates a high-confidence entity annotation set on IL side with strict searching methods; Second, uses this high-confidence set to weakly supervise the model training.
Effective LSTMs for Target-Dependent Sentiment Classification
Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence.
Aspect-Based Sentiment Analysis (ABSA)
General Classification
+2
