Papers by Dan Su
Task-Oriented Dialogue as Dataflow Synthesis (2020.tacl-1)
Copied to clipboard
Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, Alexander Zotov
| Challenge: | Existing approaches to task-oriented dialogue represent dialogue state as a dataflow graph . microsoft's SMCalFlow dataset features complex dialogues about events, weather, places, and people . |
| Approach: | They propose a dataflow graph-based dialogue agent that maps each user utterance to a program that extends this graph. |
| Outcome: | The proposed framework improves representability and predictability in natural dialogues . it uses dataflow graphs and metacomputation to map user intents to a program . |
Context Generation Improves Open Domain Question Answering (2023.findings-eacl)
Copied to clipboard
Dan Su, Mostofa Patwary, Shrimai Prabhumoye, Peng Xu, Ryan Prenger, Mohammad Shoeybi, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
| Challenge: | Existing closed-book question answering methods do not fully exploit the parameterized knowledge. |
| Approach: | They propose a closed-book QA framework which uses a coarse-to-fine approach to extract the relevant knowledge and answer a question. |
| Outcome: | The proposed method outperforms open-book QA methods on three QA benchmarks. |
Multi-hop Question Generation with Graph Convolutional Network (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on text-based QG focus on generating SQuAD-style questions. |
| Approach: | They propose a multi-hop question generation model that does context encoding in multiple hops with Graph Convolutional Network and encoder fusion via an Encoder Reasoning Gate. |
| Outcome: | Empirical results show that the proposed model generates fluent questions with high completeness and outperforms baselines on automatic evaluation metrics. |
Generalizing Question Answering System with Pre-trained Language Model Fine-tuning (D19-58)
Copied to clipboard
| Challenge: | Existing methods focus on improving in-domain performance, leaving open the question of how they can generalize to out-of-domain and unseen RC tasks. |
| Approach: | They propose a multi-task learning framework that learns the shared representation across different tasks and builds on a large pre-trained language model and fine-tuned on multiple RC datasets. |
| Outcome: | The proposed framework improves the BERT-Large baseline by 8.39 and 7.22 respectively. |
Read before Generate! Faithful Long Form Question Answering with Machine Reading (2022.findings-acl)
Copied to clipboard
| Challenge: | Long-form question answering (LFQA) generates a paragraph-length answer for a given question. |
| Approach: | They propose a framework that jointly models answer generation and machine reading. |
| Outcome: | The proposed model generates a more factually accurate answer from millions of documents retrieved from a large dataset. |
Suggest-Verify-Revise: A Three-Stage Document-Level Event Causality Identification with Narrative Consistency (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for document-level Event Causality Identification rely on local semantic similarity for independent event-pair discrimination . Existing approaches ignore the influence of the overall narrative backbone in the propagation of causal dependencies and the role differentiation of events within multi-cause/multi-effect structures. |
| Approach: | They propose a suggest-verify-revise approach for document-level Event Causality Identification with narrative consistency (SVRECI) they integrate heuristic causal suggestions generated by an LLM with structural suggestions derived from hypergraph modeling . |
| Outcome: | The proposed approach outperforms existing methods on event-storylines and Causal-TimeBank datasets. |
NusaCrowd: Open Source Initiative for Indonesian NLP Resources (2023.findings-acl)
Copied to clipboard
Samuel Cahyawijaya, Holy Lovenia, Alham Fikri Aji, Genta Winata, Bryan Wilie, Fajri Koto, Rahmad Mahendra, Christian Wibisono, Ade Romadhony, Karissa Vincentio, Jennifer Santoso, David Moeljadi, Cahya Wirawan, Frederikus Hudi, Muhammad Satrio Wicaksono, Ivan Parmonangan, Ika Alfina, Ilham Firdausi Putra, Samsul Rahmadani, Yulianti Oenang, Ali Septiandri, James Jaya, Kaustubh Dhole, Arie Suryani, Rifki Afina Putri, Dan Su, Keith Stevens, Made Nindyatama Nityasya, Muhammad Adilazuarda, Ryan Hadiwijaya, Ryandito Diandaru, Tiezheng Yu, Vito Ghifari, Wenliang Dai, Yan Xu, Dyah Damapuspita, Haryo Wibowo, Cuk Tho, Ichwanul Karo Karo, Tirana Fatyanosa, Ziwei Ji, Graham Neubig, Timothy Baldwin, Sebastian Ruder, Pascale Fung, Herry Sujaini, Sakriani Sakti, Ayu Purwarianti
| Challenge: | Existing NLP research in Indonesian languages has been held back by factors such as language diversity, orthographic variation, resource limitation and other societal challenges. |
| Approach: | They present a collaborative initiative to collect and unify existing resources for Indonesian languages and open access to previously non-public resources. |
| Outcome: | The results show that the datasets are highly reliable and can be used to generate the first zero-shot benchmarks for natural language understanding and generation in Indonesian and the local languages of Indonesia. |
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)
Copied to clipboard
| Challenge: | Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. |
| Approach: | They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture. |
| Outcome: | The proposed model outperforms existing speech language models in word error rate, speech quality, and speaker similarity. |
GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
| Approach: | They propose to use GSM-Noise to refine inputs before engaging in in-depth analysis to improve LLM robustness under noisy conditions. |
| Outcome: | The proposed model can achieve consistent performance gains under noisy conditions with prompt engineering, supervised finetuning, and reinforcement learning. |
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance (2021.findings-acl)
Copied to clipboard
| Challenge: | Query focused summarization models aim to generate summaries from source documents that can answer the given query. |
| Approach: | They propose a QFS-BART model that incorporates the explicit answer relevance of the source documents given the query via a question answering model. |
| Outcome: | Empirical results show that the proposed model achieves the new state-of-the-art performance. |
Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training (2023.eacl-main)
Copied to clipboard
| Challenge: | Large-scale vision-language pre-trained (VLP) models generate unfaithful or nonsensical texts given the source input, which is called hallucination. |
| Approach: | They propose a VLP loss-based model to mitigate object hallucination by decoupling VLP objectives and a token-level image-text alignment. |
| Outcome: | The proposed model reduces object hallucination by 17.4% on two benchmarks. |
MM-LLMs: Recent Advances in MultiModal Large Language Models (2024.findings-acl)
Copied to clipboard
| Challenge: | MultiModal Large Language Models (MM-LLMs) have undergone significant advances in the past year . traditional MM models incur substantial computational costs, especially when trained from scratch . |
| Approach: | They propose a taxonomy encompassing 126 MM-LLMs and summarize key training recipes to enhance their potency. |
| Outcome: | The proposed models preserve the reasoning and decision-making capabilities of LLMs and empower diverse range of MM tasks. |
Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset (2025.acl-long)
Copied to clipboard
Dan Su, Kezhi Kong, Ying Lin, Joseph Jennings, Brandon Norick, Markus Kliegl, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
| Challenge: | Recent Common Crawl datasets remove 90% of data, limiting their suitability for long token horizon training. |
| Approach: | They propose to combine classifier ensembling, synthetic data rephrasing and heuristic filters to achieve better trade-offs between accuracy and data quantity. |
| Outcome: | The proposed model-based filtering improves MMLU by 5.6 over DCLM for 15T tokens . the full 6.3T token dataset matches DCLM on MMLO, but contains four times more unique real tokens than DCLM . |
A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners (2024.emnlp-main)
Copied to clipboard
Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie Su, Camillo Taylor, Dan Roth
| Challenge: | a new hypothesis-testing framework is developed to assess whether large language models possess genuine reasoning abilities or primarily depend on token bias. |
| Approach: | They propose a framework to assess whether large language models have genuine reasoning abilities or primarily depend on token bias. |
| Outcome: | The proposed framework outlines a list of hypotheses where token biases are readily identifiable . the results suggest that most LLMs still struggle with logical reasoning . |