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Found 20 papers, 13 papers with code
Improving Multilingual Neural Machine Translation with Auxiliary Source Languages
Multilingual neural machine translation models typically handle one source language at a time.
SWI: Speaking with Intent in Large Language Models
Intent, typically clearly formulated and planned, functions as a cognitive framework for reasoning and problem-solving.
ARR: Question Answering with Large Language Models via Analyzing, Retrieving, and Reasoning
This paper introduces ARR, an intuitive and effective zero-shot prompting method that explicitly incorporates three key steps in QA solving: analyzing the intent of the question, retrieving relevant information, and reasoning step by step.
FuzzCoder: Byte-level Fuzzing Test via Large Language Model
In this work, we propose to adopt fine-tuned large language models (FuzzCoder) to learn patterns in the input files from successful attacks to guide future fuzzing explorations.
UniCoder: Scaling Code Large Language Model via Universal Code
When applying LLMs for code generation, recent works mainly focus on directing the models to articulate intermediate natural-language reasoning steps, as in chain-of-thought (CoT) prompting, and then output code with the natural language or other structured intermediate steps.
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt
Our method aims to minimize the representation distance of different languages by regarding the image as a central language.
Red Teaming Visual Language Models
Additionally, we simply apply red teaming alignment to LLaVA-v1. 5 with Supervised Fine-tuning (SFT) using RTVLM, and this bolsters the models' performance with 10% in RTVLM test set, 13% in MM-Hal, and without noticeable decline in MM-Bench, overpassing other LLaVA-based models with regular alignment data.
MT4CrossOIE: Multi-stage Tuning for Cross-lingual Open Information Extraction
Cross-lingual open information extraction aims to extract structured information from raw text across multiple languages.
FinPT: Financial Risk Prediction with Profile Tuning on Pretrained Foundation Models
To tackle these issues, we propose FinPT and FinBench: the former is a novel approach for financial risk prediction that conduct Profile Tuning on large pretrained foundation models, and the latter is a set of high-quality datasets on financial risks such as default, fraud, and churn.
M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M$^3$IT) dataset, designed to optimize VLM alignment with human instructions.
TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Models
In addition, generative data augmentation (GDA) has been shown to produce more diverse and flexible data.
HanoiT: Enhancing Context-aware Translation via Selective Context
Context-aware neural machine translation aims to use the document-level context to improve translation quality.
GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator
Inspired by the idea of Generative Adversarial Networks (GANs), we propose a GAN-style model for encoder-decoder pre-training by introducing an auxiliary discriminator, unifying the ability of language understanding and generation in a single model.
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation
Specifically, the target sequence is first translated into the source language and then tagged by a source NER model.
GTrans: Grouping and Fusing Transformer Layers for Neural Machine Translation
Transformer structure, stacked by a sequence of encoder and decoder network layers, achieves significant development in neural machine translation.
HLT-MT: High-resource Language-specific Training for Multilingual Neural Machine Translation
Nonetheless, multilingual training is plagued by language interference degeneration in shared parameters because of the negative interference among different translation directions, especially on high-resource languages.
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine Translation
Most translation tasks among languages belong to the zero-resource translation problem where parallel corpora are unavailable.
Exploring Entity Interactions for Few-Shot Relation Learning (Student Abstract)
Few-shot relation learning refers to infer facts for relations with a limited number of observed triples.
Multilingual Agreement for Multilingual Neural Machine Translation
Although multilingual neural machine translation (MNMT) enables multiple language translations, the training process is based on independent multilingual objectives.
Toward Tweet Entity Linking with Heterogeneous Information Networks
The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets.
