Search Results for author:
Found 13 papers, 10 papers with code
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Compared with open-source VLMs, smaller-sized scoring models can provide better feedback regarding text-image alignment and image quality, while VLMs provide more accurate feedback regarding safety and generation bias due to their stronger reasoning capabilities.
Towards Faithful Explanations: Boosting Rationalization with Shortcuts Discovery
Since existing methods still suffer from adopting the shortcuts in data to compose rationales and limited large-scale annotated rationales by human, in this paper, we propose a Shortcuts-fused Selective Rationalization (SSR) method, which boosts the rationalization by discovering and exploiting potential shortcuts.
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks
To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recognition (ASR) and speech translation (ST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation
This study investigates how Large Language Models (LLMs) leverage source and reference data in machine translation evaluation task, aiming to better understand the mechanisms behind their remarkable performance in this task.
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems
We present IMTLab, an open-source end-to-end interactive machine translation (IMT) system platform that enables researchers to quickly build IMT systems with state-of-the-art models, perform an end-to-end evaluation, and diagnose the weakness of systems.
FedJudge: Federated Legal Large Language Model
To this end, in this paper, we propose the first Federated Legal Large Language Model (FedJudge) framework, which fine-tunes Legal LLMs efficiently and effectively.
Nearest Neighbor Machine Translation is Meta-Optimizer on Output Projection Layer
Nearest Neighbor Machine Translation ($k$NN-MT) has achieved great success in domain adaptation tasks by integrating pre-trained Neural Machine Translation (NMT) models with domain-specific token-level retrieval.
Federated Nearest Neighbor Machine Translation
To protect user privacy and meet legal regulations, federated learning (FL) is attracting significant attention.
Simple and Scalable Nearest Neighbor Machine Translation
$k$NN-MT is a straightforward yet powerful approach for fast domain adaptation, which directly plugs pre-trained neural machine translation (NMT) models with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
Non-Parametric Domain Adaptation for End-to-End Speech Translation
End-to-End Speech Translation (E2E-ST) has received increasing attention due to the potential of its less error propagation, lower latency, and fewer parameters.
Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement
In this paper, we attempt to model the joint probability of transcription and translation based on the speech input to directly leverage such triplet data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Rethinking Zero-shot Neural Machine Translation: From a Perspective of Latent Variables
However, it usually suffers from capturing spurious correlations between the output language and language invariant semantics due to the maximum likelihood training objective, leading to poor transfer performance on zero-shot translation.
Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis
Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making.
