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Found 44 papers, 23 papers with code
Tencent Translation System for the WMT21 News Translation Task
Based on our success in the last WMT, we continuously employed advanced techniques such as large batch training, data selection and data filtering.
Redistributing Low-Frequency Words: Making the Most of Monolingual Data in Non-Autoregressive Translation
In this work, we provide an appealing alternative for NAT – monolingual KD, which trains NAT student on external monolingual data with AT teacher trained on the original bilingual data.
Tencent AI Lab Machine Translation Systems for the WMT20 Biomedical Translation Task
This paper describes the Tencent AI Lab submission of the WMT2020 shared task on biomedical translation in four language directions: German<->English, English<->German, Chinese<->English and English<->Chinese.
Tencent Neural Machine Translation Systems for the WMT20 News Translation Task
This paper describes Tencent Neural Machine Translation systems for the WMT 2020 news translation tasks.
Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task
This paper describes the Tencent AI Lab’s submission of the WMT 2020 shared task on chat translation in English-German.
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge.
Anchor-based Large Language Models
Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation.
Benchmarking LLMs via Uncertainty Quantification
The proliferation of open-source Large Language Models (LLMs) from various institutions has highlighted the urgent need for comprehensive evaluation methods.
Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models
This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models (LLMs): domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search.
DrugAssist: A Large Language Model for Molecule Optimization
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery.
On Diversified Preferences of Large Language Model Alignment
Our analysis reveals a correlation between the calibration performance of reward models (RMs) and the alignment performance of LLMs.
Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models
The advancement of Large Language Models (LLMs) has brought substantial attention to the Chain of Thought (CoT) approach, primarily due to its ability to enhance the capability of LLMs on complex reasoning tasks.
Alternate Diverse Teaching for Semi-supervised Medical Image Segmentation
To mitigate the confirmation bias from the diverse supervision, the core of AD-MT lies in two proposed modules: the Random Periodic Alternate (RPA) Updating Module and the Conflict-Combating Module (CCM).
GPT4Video: A Unified Multimodal Large Language Model for lnstruction-Followed Understanding and Safety-Aware Generation
While the recent advances in Multimodal Large Language Models (MLLMs) constitute a significant leap forward in the field, these models are predominantly confined to the realm of input-side multimodal comprehension, lacking the capacity for multimodal content generation.
A Comprehensive Evaluation of GPT-4V on Knowledge-Intensive Visual Question Answering
The emergence of multimodal large models (MLMs) has significantly advanced the field of visual understanding, offering remarkable capabilities in the realm of visual question answering (VQA).
Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs
To foster progress in this domain, we hold a new shared task at WMT 2023, the first edition of the Discourse-Level Literary Translation.
A Systematic Evaluation of GPT-4V's Multimodal Capability for Medical Image Analysis
This work conducts an evaluation of GPT-4V's multimodal capability for medical image analysis, with a focus on three representative tasks of radiology report generation, medical visual question answering, and medical visual grounding.
A Benchmark for Text Expansion: Datasets, Metrics, and Baselines
This work presents a new task of Text Expansion (TE), which aims to insert fine-grained modifiers into proper locations of the plain text to concretize or vivify human writings.
TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild
We release our dataset, model, and demo to foster future research in the area of multimodal instruction following.
Ranked #89 on
Visual Question Answering
on MM-Vet
Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.
Disco-Bench: A Discourse-Aware Evaluation Benchmark for Language Modelling
Modeling discourse -- the linguistic phenomena that go beyond individual sentences, is a fundamental yet challenging aspect of natural language processing (NLP).
On the Cultural Gap in Text-to-Image Generation
Accordingly, we propose a novel multi-modal metric that considers object-text alignment to filter the fine-tuning data in the target culture, which is used to fine-tune a T2I model to improve cross-cultural generation.
Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration
Although instruction-tuned large language models (LLMs) have exhibited remarkable capabilities across various NLP tasks, their effectiveness on other data modalities beyond text has not been fully studied.
How Does Pretraining Improve Discourse-Aware Translation?
To bridge this gap, we introduce a probing task to interpret the ability of PLMs to capture discourse relation knowledge.
TaleCrafter: Interactive Story Visualization with Multiple Characters
Accurate Story visualization requires several necessary elements, such as identity consistency across frames, the alignment between plain text and visual content, and a reasonable layout of objects in images.
Revisiting Non-Autoregressive Translation at Scale
Experimental results on the large-scale WMT20 En-De show that the asymmetric architecture (e. g. bigger encoder and smaller decoder) can achieve comparable performance with the scaling model, while maintaining the superiority of decoding speed with standard NAT models.
Deepfake Text Detection in the Wild
In practical scenarios, the detector faces texts from various domains or LLMs without knowing their sources.
A Survey on Zero Pronoun Translation
Zero pronouns (ZPs) are frequently omitted in pro-drop languages (e. g. Chinese, Hungarian, and Hindi), but should be recalled in non-pro-drop languages (e. g. English).
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models
We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.
Prompt-Learning for Cross-Lingual Relation Extraction
Experiments on WMT17-EnZh XRE also show the effectiveness of our Prompt-XRE against other competitive baselines.
Document-Level Machine Translation with Large Language Models
Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks.
Search-Engine-augmented Dialogue Response Generation with Cheaply Supervised Query Production
We propose a dialogue model that can access the vast and dynamic information from any search engine for response generation.
Large Language Models Meet Harry Potter: A Bilingual Dataset for Aligning Dialogue Agents with Characters
In this paper, we introduce the Harry Potter Dialogue (HPD) dataset, designed to advance the study of dialogue agents and character alignment.
ngram-OAXE: Phrase-Based Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation
Recently, a new training oaxe loss has proven effective to ameliorate the effect of multimodality for non-autoregressive translation (NAT), which removes the penalty of word order errors in the standard cross-entropy loss.
On the Complementarity between Pre-Training and Back-Translation for Neural Machine Translation
Pre-training (PT) and back-translation (BT) are two simple and powerful methods to utilize monolingual data for improving the model performance of neural machine translation (NMT).
On the Copying Behaviors of Pre-Training for Neural Machine Translation
In response to this problem, we propose a simple and effective method named copying penalty to control the copying behaviors in decoding.
Progressive Multi-Granularity Training for Non-Autoregressive Translation
Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence.
Rejuvenating Low-Frequency Words: Making the Most of Parallel Data in Non-Autoregressive Translation
Results demonstrate that the proposed approach can significantly and universally improve translation quality by reducing translation errors on low-frequency words.
TranSmart: A Practical Interactive Machine Translation System
Automatic machine translation is super efficient to produce translations yet their quality is not guaranteed.
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning
Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks.
Understanding and Improving Lexical Choice in Non-Autoregressive Translation
To this end, we introduce an extra Kullback-Leibler divergence term derived by comparing the lexical choice of NAT model and that embedded in the raw data.
Context-Aware Cross-Attention for Non-Autoregressive Translation
Non-autoregressive translation (NAT) significantly accelerates the inference process by predicting the entire target sequence.
On the Sub-Layer Functionalities of Transformer Decoder
There have been significant efforts to interpret the encoder of Transformer-based encoder-decoder architectures for neural machine translation (NMT); meanwhile, the decoder remains largely unexamined despite its critical role.
On the Sparsity of Neural Machine Translation Models
Modern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources.
