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Found 40 papers, 21 papers with code
HI-CMLM: Improve CMLM with Hybrid Decoder Input
Mask-predict CMLM (Ghazvininejad et al., 2019) has achieved stunning performance among non-autoregressive NMT models, but we find that the mechanism of predicting all of the target words only depending on the hidden state of [MASK] is not effective and efficient in initial iterations of refinement, resulting in ungrammatical repetitions and slow convergence.
HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation
For machine translation part, we pretrained three translation models on WMT21 dataset and fine-tuned them on in-domain corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels due to its subjectivity.
How Length Prediction Influence the Performance of Non-Autoregressive Translation?
Length prediction is a special task in a series of NAT models where target length has to be determined before generation.
Noisy Label Regularisation for Textual Regression
Training with noisy labelled data is known to be detrimental to model performance, especially for high-capacity neural network models in low-resource domains.
Learning from Unlabelled Data for Clinical Semantic Textual Similarity
Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning.
The HW-TSC’s Speech to Speech Translation System for IWSLT 2022 Evaluation
The paper presents the HW-TSC’s pipeline and results of Offline Speech to Speech Translation for IWSLT 2022.
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI
To verify the generalizability of this finding across languages and domains, we perform an extensive case study to identify the upper bound of human detection accuracy.
GenAI Content Detection Task 1: English and Multilingual Machine-Generated Text Detection: AI vs. Human
We present the GenAI Content Detection Task~1 -- a shared task on binary machine generated text detection, conducted as a part of the GenAI workshop at COLING 2025.
Enhancing Visual Representation for Text-based Person Searching
Prior works adopt image and text encoders pre-trained on unimodal data to extract global and local features from image and text respectively, and then global-local alignment is achieved explicitly.
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety.
Detection of Human and Machine-Authored Fake News in Urdu
The rise of social media has amplified the spread of fake news, now further complicated by large language models (LLMs) like ChatGPT, which ease the generation of highly convincing, error-free misinformation, making it increasingly challenging for the public to discern truth from falsehood.
Arabic Dataset for LLM Safeguard Evaluation
The growing use of large language models (LLMs) has raised concerns regarding their safety.
FIRE: Fact-checking with Iterative Retrieval and Verification
The typical approach to fact-checking these atomic claims involves retrieving a fixed number of pieces of evidence, followed by a verification step.
Loki: An Open-Source Tool for Fact Verification
We introduce Loki, an open-source tool designed to address the growing problem of misinformation.
LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection
Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education.
OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs
The increased use of large language models (LLMs) across a variety of real-world applications calls for automatic tools to check the factual accuracy of their outputs, as LLMs often hallucinate.
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs
Our systematic exploration of LMs' understanding of empathy reveals substantial opportunities for further investigation in both task formulation and modeling.
Exploring the Potential of Multimodal LLM with Knowledge-Intensive Multimodal ASR
Recent advancements in multimodal large language models (MLLMs) have made significant progress in integrating information across various modalities, yet real-world applications in educational and scientific domains remain challenging.
OpenFactCheck: Building, Benchmarking Customized Fact-Checking Systems and Evaluating the Factuality of Claims and LLMs
To mitigate these issues, we propose OpenFactCheck, a unified framework for building customized automatic fact-checking systems, benchmarking their accuracy, evaluating factuality of LLMs, and verifying claims in a document.
SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection
The task attracted a large number of participants: subtask A monolingual (126), subtask A multilingual (59), subtask B (70), and subtask C (30).
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safe use as various vulnerabilities are exposed.
A Chinese Dataset for Evaluating the Safeguards in Large Language Models
Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs.
M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection
The advent of Large Language Models (LLMs) has brought an unprecedented surge in machine-generated text (MGT) across diverse channels.
Conversational SimulMT: Efficient Simultaneous Translation with Large Language Models
Simultaneous machine translation (SimulMT) presents a challenging trade-off between translation quality and latency.
Factuality of Large Language Models: A Survey
Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting, and integrating information from multiple sources by offering a straightforward answer to a variety of questions in a single place.
Demystifying Instruction Mixing for Fine-tuning Large Language Models
Instruction tuning significantly enhances the performance of large language models (LLMs) across various tasks.
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers
The increased use of large language models (LLMs) across a variety of real-world applications calls for mechanisms to verify the factual accuracy of their outputs.
A Survey of Confidence Estimation and Calibration in Large Language Models
Assessing their confidence and calibrating them across different tasks can help mitigate risks and enable LLMs to produce better generations.
Rethinking STS and NLI in Large Language Models
Recent years have seen the rise of large language models (LLMs), where practitioners use task-specific prompts; this was shown to be effective for a variety of tasks.
Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs
With the rapid evolution of large language models (LLMs), new and hard-to-predict harmful capabilities are emerging.
Collective Human Opinions in Semantic Textual Similarity
Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as the gold standard.
M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection
These results show that the problem is far from solved and that there is a lot of room for improvement.
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encoder layers exceeds 18.
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation
An effective training strategy to improve the performance of AT models is Self-Distillation Mixup (SDM) Training, which pre-trains a model on raw data, generates distilled data by the pre-trained model itself and finally re-trains a model on the combination of raw data and distilled data.
Diformer: Directional Transformer for Neural Machine Translation
In this paper, we aim to close the gap by preserving the original objective of AR and NAR under a unified framework.
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
This paper describes our work in participation of the IWSLT-2021 offline speech translation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Evaluating the Utility of Model Configurations and Data Augmentation on Clinical Semantic Textual Similarity
In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain.
