Papers by David Huang
Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention (N18-1)
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| Challenge: | Neural machine translation models learn to map from source language sentences to target language sentences via continuous-space intermediate representations. |
| Approach: | They propose an encoder with character attention which augments the (sub)word-level representation with character-level information and a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively. |
| Outcome: | The proposed model outperforms the standard word-based model, subword-based models, and strong character-based ones on translation tasks. |
Image Difference Captioning via Adversarial Preference Optimization (2025.emnlp-main)
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| Challenge: | Existing supervised approaches to image difference captioning overfit to dataset-specific language patterns and fail to capture accurate preferences. |
| Approach: | They propose an adversarial direct preference optimization framework that aligns captioning policy with pairwise difference preferences via Direct Preference Optimization. |
| Outcome: | The proposed approach outperforms baselines on benchmark IDC datasets in generating fine-grained and accurate difference descriptions. |
Stronger Universal and Transferable Attacks by Suppressing Refusals (2025.naacl-long)
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| Challenge: | Efforts have focused on aligning models to human preferences (RLHF) . yet, it is believed that such optimization-based attacks are sample-specific. |
| Approach: | They propose an algorithm to embed a "safety feature" into models to make them safe for mass deployment. |
| Outcome: | The proposed attack achieves 25% success rate against the state-of-the-art Circuit Breaker defense, compared to 2.5% by white-box GCG. |
VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wild (2024.acl-long)
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| Challenge: | VoiceCraft is a token-infilling neural codec language model for speech editing and zero-shot text-to-speech evaluation. |
| Approach: | They introduce a token infilling neural codec language model that performs on speech editing and zero-shot text-to-speech tasks. |
| Outcome: | The proposed model outperforms previous models on speech editing and zero-shot text-to-speech tasks. |
Language Models Hallucinate, but May Excel at Fact Verification (2024.naacl-long)
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| Challenge: | Recent advances in large language models (LLMs) have produced non-factual outputs . however, current LLMs suffer from the hallucination issue . |
| Approach: | They propose to use instruction-tuned LLMs to generate factual outputs . they find that FLAN-T5-11B performs best as a fact verifier . |
| Outcome: | The proposed method outperforms more capable LLMs like GPT3.5 and ChatGPT in the human evaluation. |
Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)
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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu
| Challenge: | Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent. |
| Approach: | They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs. |
| Outcome: | The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models. |
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment (2026.acl-industry)
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Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman, Naveen Suda, David Eriksson, Maximilian Balandat, Rylan Conway, Patrick Huber, Chinnadhurai Sankar, Ayushi Dalmia, Zechun Liu, Lemeng Wu, Tarek Elgamal, Adithya Sagar, Vikas Chandra, Raghuraman Krishnamoorthi
| Challenge: | MobileLLM-Flash is a family of foundation models for efficient on-device use with strong capabilities. |
| Approach: | They propose a method for designing on-device large language models under mobile latency constraints using hardware-in-the-loop architecture search. |
| Outcome: | The proposed model is amenable to industry-scale deployment and is compatible with mobile runtimes like Executorch. |
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments (2026.acl-long)
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Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, Angelika Romanou, Antoni-Joan Solergibert, Barna Pásztor, Bettina Messmer, Dhia Garbaya, Eduard Frank Ďurech, Ido Hakimi, Juan Garcia Giraldo, Mete Ismayilzada, Negar Foroutan, Skander Moalla, Tiancheng Chen, Vinko Sabolčec, Yixuan Xu, Michael Aerni, Badr AlKhamissi, Inés Altemir Marinas, Mohammad Hossein Amani, Matin Ansaripour, Ilia Badanin, Harold Benoit, Emanuela Boros, Nicholas John Browning, Fabian Bösch, Maximilian Böther, Niklas Canova, Camille Challier, Clément Charmillot, Jonathan Coles, Jan Milan Deriu, Arnout Devos, Lukas Drescher, Daniil Dzenhaliou, Maud Ehrmann, Dongyang Fan, Simin Fan, Silin Gao, Miguel Gila, María Grandury, Diba Hashemi, Alexander Miserlis Hoyle, Jiaming Jiang, Mark Klein, Andrei Kucharavy, Anastasiia Kucherenko, Frederike Lübeck, Roman Machacek, Theofilos Ioannis Manitaras, Andreas Marfurt, Kyle Matoba, Simon Matrenok, Henrique Mendonça, Fawzi Roberto Mohamed, Syrielle Montariol, Luca Mouchel, Sven Najem-Meyer, Jingwei Ni, Gennaro Oliva, Matteo Pagliardini, Elia Palme, Andrei Panferov, Léo Paoletti, Marco Passerini, Ivan Pavlov, Auguste Poiroux, Kaustubh Ponkshe, Nathan Ranchin, Javier Rando, Mathieu Sauser, Jakhongir Saydaliev, Mukhammadali Sayfiddinov, Marian Schneider, Stefano Schuppli, Marco Scialanga, Andrei Semenov, Kumar Shridhar, Raghav Singhal, Anna Sotnikova, Alexander Sternfeld, Ayush Kumar Tarun, Paul Teiletche, Jannis Vamvas, Xiaozhe Yao, Hao Zhao, Alexander Ilic, Ana Klimovic, Andreas Krause, Caglar Gulcehre, David Rosenthal, Elliott Ash, Florian Tramèr, Joost VandeVondele, Livio Veraldi, Martin Rajman, Thomas C. Schulthess, Torsten Hoefler, Antoine Bosselut, Martin Jaggi, Imanol Schlag
| Challenge: | Apertus is a fully open suite of large language models (LLMs) designed to address responsibility shortcomings in today’s open model ecosystem, namely data responsibility and global representation. |
| Approach: | They propose to release a fully open suite of large language models (LLMs) that address data responsibility and global representation shortcomings in today’s open model ecosystem. |
| Outcome: | The proposed model is pretrained on openly available data and suppresses verbatim recall of data while retaining task performance. |
ClaimLens: Automated, Explainable Fact-Checking on Voting Claims Using Frame-Semantics (2024.emnlp-demo)
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| Challenge: | Existing fact-checking solutions lack transparency and explainability . a lack of transparency can make it difficult for users to trust and understand the reasoning behind the outcomes. |
| Approach: | They propose an automated fact-checking system focused on voting-related factual claims that leverages frame-semantic parsing to provide structured and interpretable fact verification. |
| Outcome: | The proposed system can extract relevant information from voting-related factual claims using public records and Vote semantic frame. |
SecureVibeBench: Benchmarking Secure Vibe Coding of AI Agents via Reconstructing Vulnerability-Introducing Scenarios (2026.acl-long)
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Junkai Chen, Huihui Huang, Yunbo Lyu, Junwen An, Jieke Shi, Chengran Yang, Ting Zhang, Haoye Tian, Yikun Li, Zhenhao Li, Xin Zhou, Xing Hu, David Lo
| Challenge: | Existing benchmarks fail to capture scenarios in which vulnerabilities are introduced by humans . we evaluate 5 popular code agents supported by 5 LLMs on SecureVibeBench . |
| Approach: | They propose a benchmarking tool that compares 105 C/C++ secure coding tasks . they use real-world open-source vulnerabilities and a comprehensive evaluation tool . |
| Outcome: | The proposed benchmarks show that code agents struggle to produce correct and secure code . the best performing agent produces merely 23.8% correct and secured solutions . |