Papers by Zhen Hu
Composable Text Controls in Latent Space with ODEs (2023.emnlp-main)
Copied to clipboard
Guangyi Liu, Zeyu Feng, Yuan Gao, Zichao Yang, Xiaodan Liang, Junwei Bao, Xiaodong He, Shuguang Cui, Zhen Li, Zhiting Hu
| Challenge: | Existing approaches to composable text operations often require plug-and-play . a single LM can perform arbitrary text operation composition in the latent space . |
| Approach: | They propose an efficient approach for composable text operations in the latent space of text . they connect pretrained LMs to the laten space and adapt them to the space . |
| Outcome: | The proposed approach improves on existing methods in the latent space of text. |
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)
Copied to clipboard
Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu
| Challenge: | closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges . |
| Approach: | They propose a framework that leverages collective intelligence from all large language models to evaluate each other. |
| Outcome: | a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost. |
Modalities Should Be Appropriately Leveraged: Uncertainty Guidance for Multimodal Chinese Spelling Correction (2024.lrec-main)
Copied to clipboard
| Challenge: | Chinese spelling correction (CSC) aims to detect and correct spelling errors in Chinese texts. |
| Approach: | They propose a framework that incorporates uncertainty into feature learning and correction stages . they propose to combine the uncertainty of multimodal features with model learning . |
| Outcome: | The proposed framework improves on three public datasets. |
E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition (2023.findings-acl)
Copied to clipboard
Zhen Zhang, Mengting Hu, Shiwan Zhao, Minlie Huang, Haotian Wang, Lemao Liu, Zhirui Zhang, Zhe Liu, Bingzhe Wu
| Challenge: | Named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty. |
| Approach: | They propose to introduce two uncertainty-guided loss terms to the conventional EDL and a series of uncertainty-guiding training strategies to solve these challenges. |
| Outcome: | The proposed method achieves better OOV/OOD detection performance and generalization ability on OOV entities compared to state-of-the-art methods. |
Semantic and Sentiment Dual-Enhanced Generative Model for Script Event Prediction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods to model event associations struggle with semantic ambiguity and embedding bias. |
| Approach: | They propose a Semantic and Sentiment Dual-enhanced Generative Model to address these issues . it leverages two types of script event information to enhance the generative model . |
| Outcome: | The proposed model captures both global and local sentiments of events through its sentiment awareness mechanism. |
Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to reading comprehension systems are vulnerable to adversarial attacks. |
| Approach: | They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model. |
| Outcome: | The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks. |
Graph Enhanced Contrastive Learning for Radiology Findings Summarization (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods for automating impression generation have limited the relationship between extra knowledge and the original findings. |
| Approach: | They propose a framework for automating impression generation that exploits extra knowledge and original findings . they propose combining key words and their relations to extract critical information . |
| Outcome: | The proposed framework exploits extra knowledge and the original findings in an integrated way . the state-of-the-art results on two datasets confirm the effectiveness of the proposed method . |
Reasoning with Language Model is Planning with World Model (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown remarkable reasoning capabilities, particularly with Chain-of-Thought-style prompts. |
| Approach: | They propose a framework that repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) |
| Outcome: | The proposed framework repurposes the LLM as both a world model and a reasoning agent and incorporates a principled planning algorithm (based on Monte Carlo Tree Search) it achieves optimum balance between exploration and exploitation, while achieving high-reward reasoning paths efficiently. |
OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models (2023.acl-demo)
Copied to clipboard
| Challenge: | Existing implementations that modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM have limited the practicality and flexibility of delta tuning. |
| Approach: | They propose an open-source library that provides a plug-and-play implementation of delta tuning methods for pre-trained models. |
| Outcome: | The proposed methods eliminate the need to modify the backbone PTMs’ code, making OpenDelta compatible with different, even novel PTM. |
Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing studies focus on what to generate but ignore what not to generate . a template-agnostic method boosts original learning and reduces mistakes simultaneously . |
| Approach: | They propose a template-agnostic method to control the token-level generation . they introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models . |
| Outcome: | The proposed method boosts original learning and reduces mistakes simultaneously on four public datasets. |
Improving Continual Relation Extraction through Prototypical Contrastive Learning (2022.coling-1)
Copied to clipboard
| Challenge: | Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. |
| Approach: | They propose a Continual Relation Extraction framework with Contrastive Learning which is built with a classification network and a prototypical contrastive network to achieve incremental-class learning of CRE. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two public datasets and proves its effectiveness on improving performance. |
RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios (2025.findings-emnlp)
Copied to clipboard
Fei Zhao, Chengqiang Lu, Yufan Shen, Qimeng Wang, Yicheng Qian, Haoxin Zhang, Yan Gao, null Yiwu, Yao Hu, Zhen Wu, Shangyu Xing, Xinyu Dai
| Challenge: | RealBench is the first Chinese multimodal multi-image dataset . the dataset contains 9393 samples and 69910 images . |
| Approach: | They propose to create a Chinese multimodal multi-image dataset using 21 models . they use closed-source models that support multi-inputs as well as open-source visual and video models a . |
| Outcome: | The first Chinese multimodal multi-image dataset contains 9393 samples and 69910 images. |
Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Empirical evaluations on eight recent LLMs reveal that DRPO significantly enhances alignment performance, enabling base models to outperform their SFT/RLHF-tuned counterparts. |
| Approach: | They propose a tuning-free approach to self-alignment called Dynamic Rewarding with Prompt Optimization (DRPO) it leverages a dynamic rewarding mechanism to identify and rectify alignment weaknesses . |
| Outcome: | The proposed approach outperforms existing methods and is highly adaptable to various alignment challenges. |
KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)
Copied to clipboard
Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang
| Challenge: | Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question . |
| Approach: | They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance . |
| Outcome: | The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered . |
TRUST: Towards Robust Social Bot Detection via Uncertainty-Guided Pseudo-Labeling and Graph Structure Purification (2026.findings-acl)
Copied to clipboard
Ruixuan Xu, Mengting Hu, Zhunheng Wang, Ming Jiang, Rui Ying, Zhen Zhang, Hang Gao, Shuaipeng Liu, Renhong Cheng
| Challenge: | Existing graph-based detection models are vulnerable to deceptive message propagation, where bots deliberately interact with legitimate users. |
| Approach: | They propose a framework to mitigate deceptive message propagation by node-level uncertainty estimation and graph structure purification. |
| Outcome: | The proposed framework improves on three benchmark datasets and six GNN backbones on real-world social bots. |
CSP:Code-Switching Pre-training for Neural Machine Translation (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to train neural machine translation models are data-hungry and low-resource . et al., 2018; Radford e.t., 2019; Yang ee.,2019) proposes a new pre-training method for NMT . |
| Approach: | They propose a new pre-training method which randomly replaces some words in the input sentence with their translation words in target language. |
| Outcome: | The proposed method improves on unsupervised and supervised NMT models by making full use of monolingual corpora. |
Distill, Fuse, Pre-train: Towards Effective Event Causality Identification with Commonsense-Aware Pre-trained Model (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to detect causal relationships in unstructured texts ignore trivial knowledge which may prejudice performance. |
| Approach: | They propose a pipeline to build a commonsense-aware pre-trained model which integrates reliable task-specific knowledge from commonsens graphs. |
| Outcome: | The proposed pipeline integrates reliable task-specific knowledge from commonsense graphs. |
SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)
Copied to clipboard
Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi
| Challenge: | SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Approach: | They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models. |
| Outcome: | The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice. |
LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing (2026.findings-acl)
Copied to clipboard
Hao Li, Yiqun Zhang, Zhaoyan Guo, Chenxu Wang, Shengji Tang, Qiaosheng Zhang, Yang Chen, Biqing Qi, Peng Ye, Lei Bai, Zhen Wang, Shuyue Hu
| Challenge: | Large language model (LLM) routing assigns each query to the best suitable model from an ensemble. |
| Approach: | They introduce a large-scale benchmark and unified framework for LLM routing . they find that many routing methods exhibit similar performance under unified evaluation . |
| Outcome: | The proposed benchmark provides comprehensive metrics for both performance-oriented and performance-cost trade-off routing. |
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to target sentiment analysis are limited by huge search space and sentiment inconsistency. |
| Approach: | They propose a span-based extract-then-classify framework to detect opinion targets . they propose pipeline, joint, and collapsed models to classify polarities . |
| Outcome: | The proposed framework outperforms the sequence tagging baseline on three benchmark datasets. |
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation (2022.naacl-main)
Copied to clipboard
Guangyi Liu, Zichao Yang, Tianhua Tao, Xiaodan Liang, Junwei Bao, Zhen Li, Xiaodong He, Shuguang Cui, Zhiting Hu
| Challenge: | Neural text generation models are typically trained by maximizing log-likelihood with the sequence cross entropy (CE) loss. |
| Approach: | They propose an Edit-Invariant Sequence Loss method which computes the matching loss of a target sequence with all n-grams in the generated sequence. |
| Outcome: | The proposed method outperforms the common CE loss and strong baselines on a wide range of tasks. |
RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Various machine learning methods for tabular data lack accurate confidence estimation, which is needed for high-risk sensitive applications such as credit modeling and financial fraud detection. |
| Approach: | They propose a general post-training confidence calibration framework to calibrate the confidence of current machine learning models by employing graph neural networks to model the relationships between different samples. |
| Outcome: | The proposed framework improves the confidence estimation on tabular datasets by using graph neural networks to model the relationships between different samples. |
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)
Copied to clipboard
Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks. |
| Approach: | They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions. |
| Outcome: | The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. |
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)
Copied to clipboard
Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources. |
| Approach: | They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing. |
| Outcome: | The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus. |
MAgIC: Investigation of Large Language Model Powered Multi-Agent in Cognition, Adaptability, Rationality and Collaboration (2024.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have advanced natural language processing, demonstrating exceptional reasoning, tool usage, and memory capabilities. |
| Approach: | They propose a competition-based benchmark framework specifically designed to assess LLMs within multi-agent environments. |
| Outcome: | The proposed framework enhances the LLMs’ abilities in navigating complex social and cognitive dimensions by over threefold between the strongest and weakest LLM models. |
Adaptive Threshold Selective Self-Attention for Chinese NER (2022.coling-1)
Copied to clipboard
| Challenge: | Named entity recognition (NER) is a computationally difficult task in Chinese since there is no natural delimiter between words in sentences. |
| Approach: | They propose a data-driven Adaptive Threshold Selective Self-Attention mechanism to select the most relevant characters to enhance Transformer architecture for Chinese named entity recognition. |
| Outcome: | Experiments on four benchmark Chinese NER datasets show the proposed mechanism improves performance. |
Emotion Trajectory-aware Retrieval for Markov-driven Emotion Anticipation in LLM-based Emotional Support Conversation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing strategies focus on planning the next-turn dialogue strategies, while external strategy planners focus on generating empathetic responses. |
| Approach: | They propose a Markov-driven emotion anticipation framework with emotion trajectory-aware retrieval for LLM-based ESC, which anticipates future emotion states to guide strategy planning and achieve sustained emotional support. |
| Outcome: | The proposed framework can anticipate future emotions and achieve sustained emotional support on two datasets with two models. |
A Multi-Type Multi-Span Network for Reading Comprehension that Requires Discrete Reasoning (D19-1)
Copied to clipboard
| Challenge: | Existing models for reading comprehension and question answering do not support discrete reasoning abilities. |
| Approach: | They propose a reading comprehension model that uses a multi-type answer predictor and a multiple-span extraction method to produce one or multiple text spans. |
| Outcome: | The proposed model achieves 79.9 F1 on the DROP hidden test set, creating new state-of-the-art results. |
T 2 -NER: A Two-Stage Span-Based Framework for Unified Named Entity Recognition with Templates (2023.tacl-1)
Copied to clipboard
| Challenge: | Named Entity Recognition (NER) has evolved from flat to overlapped and discontinuous . NER is a text recognition task that recognizes mentions that represent entities in text . |
| Approach: | They propose a two-stage span-based framework to solve a unified NER task using two stages . they extract entity spans, classify over all entity span pairs and combine them to train two stages. |
| Outcome: | The proposed framework beats all the current competitive baselines on eight benchmark datasets, obtaining the best performance of unified NER. |
CoPA: Benchmarking Personalized Question Answering with Data-Informed Cognitive Factors (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing LLMs rely on surface-level similarity or manual heuristics to evaluate personalization . Existing evaluation protocols for personalization are lacking sufficient data-driven validation. |
| Approach: | They propose a benchmark to assess personalization by mining CIPDs to quantify individual preferences. |
| Outcome: | The proposed benchmark provides a more comprehensive and discriminative standard than generic metrics. |
Entropy-Aware Reshaping of Reinforcement Signals for Multi-Answer Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) is a standard post-training paradigm for large language models. |
| Approach: | They propose a framework that reshapes how learning signals are normalized and aggregated. |
| Outcome: | Experiments on MCTACO and MMLU-Multi show that the proposed framework improves accuracy, training stability and cross-dataset transfer performance. |
Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)
Copied to clipboard
Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu
| Challenge: | Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary. |
| Approach: | They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states. |
| Outcome: | The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates. |
PathwiseRAG: Multi-Dimensional Exploration and Integration Framework (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing retrieval-augmented generation systems employ rigid retrieval strategies . static retrieval produces knowledge blind spots, missing connections between quantum algorithms and encryption vulnerabilities . |
| Approach: | PathwiseRAG addresses these challenges through intent-aware strategy selection . it constructs a directed acyclic graph of interconnected sub-problems and explores multiple reasoning trajectories . |
| Outcome: | The proposed framework achieves higher accuracy and better reliability than current systems. |
KC-GenRe: A Knowledge-constrained Generative Re-ranking Method Based on Large Language Models for Knowledge Graph Completion (2024.lrec-main)
Copied to clipboard
| Challenge: | Knowledge graph completion (KGC) is a critical task to predict missing facts among entities. |
| Approach: | They propose a knowledge-constrained generative re-ranking method based on generative large language models for KGC that can predict missing facts among entities. |
| Outcome: | The proposed method achieves state-of-the-art performance on four datasets and 9.0% and 11.1% compared to the previous methods. |
Design First, Code Later: Aesthetically Pleasing Template-Free Slides Generation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to producing presentation slides rely on fixed templates or executable code . Existing methods rely only on predefined templates and emit executable codes . |
| Approach: | They propose a hierarchical slides generation workflow DeepSlides that organizes slide design tasks without any predefined template or style. |
| Outcome: | The proposed framework outperforms baseline methods on evaluated metrics and achieves superior performance in human preference evaluations. |
GraphNarrator: Generating Textual Explanations for Graph Neural Networks (2025.acl-long)
Copied to clipboard
| Challenge: | Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. |
| Approach: | They propose to use a generative language model to map input-output pairs to explanations reflecting the model’s decision-making process to generate a model that generates pseudo-labels that capture the model's decisions from saliency-based explanations. |
| Outcome: | Extensive experiments show that GraphNarrator produces human-preferred explanations that are faithful, concise, and human-like. |
Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering (2024.lrec-main)
Copied to clipboard
Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou, Zhen Wu, Yujie Chen, Shenglan Wu, Haoyuan Hu, Xinyu Dai
| Challenge: | Open Domain Multi-Hop Question Answering (ODMHQA) is one of the most challenging tasks in Natural Language Processing (NLP) |
| Approach: | They propose a mechanism that leverages the intrinsic capabilities of Large Language Models to judge whether the generated answers are off-topic. |
| Outcome: | The proposed method reduces the occurrence of off-topic answers by nearly 13%, improving the performance in Exact Match (EM) by nearly 3% compared to the baseline method without the Dr3 mechanism. |
Logical Structure as Knowledge: Enhancing LLM Reasoning via Structured Logical Knowledge Density Estimation (2026.findings-acl)
Copied to clipboard
Zhen Bi, Zhenlin Hu, Xueshu Chen, Mingyang Chen, Cheng Deng, Yida Xue, Zhen Wang, Qing Shen, Ningyu Zhang, Jungang Lou
| Challenge: | Existing data-centric paradigms equate quality with factuality or diversity and ignore the internal logical complexity of training samples. |
| Approach: | They propose a density-aware re-cognizing optimization strategy that prioritizes high-density logical samples to align training with the model's reasoning boundary. |
| Outcome: | The proposed metric outperforms existing methods and improves reasoning performance without increasing total data volume. |
Retrieve, Read, Rerank: Towards End-to-End Multi-Document Reading Comprehension (P19-1)
Copied to clipboard
| Challenge: | Existing approaches to answer reading comprehension tasks are inefficient since the input is re-encoded within each module. |
| Approach: | They propose a unified question answering model that combines context retrieving, reading comprehension, and answer reranking to predict the final answer. |
| Outcome: | The proposed model outperforms the baseline model and achieves state-of-the-art results on two versions of TriviaQA and two variants of SQuAD. |
Partial Order-centered Hyperbolic Representation Learning for Few-shot Relation Extraction (2025.coling-main)
Copied to clipboard
| Challenge: | Existing methods for few-shot relation extraction are limited to labeled instances and rely on data labeling. |
| Approach: | They propose a partial order-centered hyperbolic representation learning framework which imposes constraints on relations on instances by modeling partial order in hyperbolical space. |
| Outcome: | The proposed framework outperforms baseline methods on three benchmark datasets on 1-shot settings lacking relation descriptions. |
NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model (2025.acl-industry)
Copied to clipboard
Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Ke Hu, Szu-Wei Fu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, Chao-Han Huck Yang
| Challenge: | Existing methods to train a model on a mixture of domain datasets require separate correction language models. |
| Approach: | They propose a multi-task correction MoE that trains experts to become an "expert" of speech-to-text, language-totext and vision-to text datasets by learning to route each dataset’s tokens to its mapped expert. |
| Outcome: | The proposed model outperforms GPT-3.5 and Claude-3.5-Sonnet on the Open ASR Leaderboard and reaches an average relative 5.0% WER reduction and substantial improvements in BLEU scores. |