Papers by Bo Pang
ARM: Alignment with Residual Energy-Based Model (2024.naacl-long)
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| Challenge: | Large language models (LLMs) acquire a wide range of abilities and abilities, but their behavior does not align with human preferences. |
| Approach: | They propose to minimize a forward Kullback–Leibler divergence from a target policy to a parameteric policy instead of a reverse KL as in RLHF methods. |
| Outcome: | The proposed method can learn an aligned policy by minimizing a forward Kullback–Leibler divergence from a target policy to a parameteric policy instead of a reverse KL as in RLHF methods. |
Towards Holistic and Automatic Evaluation of Open-Domain Dialogue Generation (2020.acl-main)
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| Challenge: | Existing methods of open-domain dialogue evaluation are labor-intensive and inefficient. |
| Approach: | They propose to use open-domain dialogues to evaluate different aspects of dialogues using holistic evaluation metrics. |
| Outcome: | The proposed metrics show strong correlations with human judgments. |
SCRIPT: Self-Critic PreTraining of Transformers (2021.naacl-main)
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| Challenge: | Existing methods for representation learning of text are masked language modeling (MLM) a language model is trained to learn universal contextual embeddings, which are fine-tuned on a down-stream task. |
| Approach: | They propose a self-critic pretraining transformer for representation learning of text . they demonstrate improved sample-efficiency and improved performance over strong baselines . |
| Outcome: | The proposed model improves sample-efficiency and performance over strong baselines. |
Beyond Instructional Videos: Probing for More Diverse Visual-Textual Grounding on YouTube (2020.emnlp-main)
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| Challenge: | a representative pretraining model is fit to a diverse YouTube8M dataset . a priori, this domain is relatively easy for instructional videos . |
| Approach: | They fit a representative pretraining model to a YouTube8M dataset and examine its success and failure cases. |
| Outcome: | The proposed model can be trained on more diverse video corpora and achieve high performance on many video understanding tasks. |
End-to-end Dense Video Captioning as Sequence Generation (2022.coling-1)
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| Challenge: | Existing methods for dense video captioning use a two-stage generative process . but, more complex tasks are not able to fully utilize this powerful paradigm . |
| Approach: | They propose to model two subtasks of dense video captioning as one sequence generation task and predict the events and the corresponding descriptions. |
| Outcome: | Experiments on YouCook2 and ViTT show that the proposed model can be used on any video platform. |
Generative Text Modeling through Short Run Inference (2021.eacl-main)
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| Challenge: | Latent variable models for text capture global semantic and syntactic features when trained correctly. |
| Approach: | They propose a short run dynamics for inference that initializes from the prior distribution of the latent variable and runs a small number of Langevin dynamics steps guided by its posterior distribution. |
| Outcome: | The proposed model is able to generate coherent sentences with smooth transition and shows no sign of posterior collapse. |
LongSpec: Long-Context Lossless Speculative Decoding with Efficient Drafting and Verification (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) can process extremely long contexts, requiring efficient inference over extended inputs. |
| Approach: | They propose a model that uses a constant-sized key-value cache to train long-context models. |
| Outcome: | Experimental results show that LongSpec achieves 3.26x speedup over strong Flash Attention baselines and 2.34x wall clock time on four math reasoning tasks. |
SParC: Cross-Domain Semantic Parsing in Context (P19-1)
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Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, Emily Ji, Shreya Dixit, David Proctor, Sungrok Shim, Jonathan Kraft, Vincent Zhang, Caiming Xiong, Richard Socher, Dragomir Radev
| Challenge: | Xu et al., 2017): a dataset for cross-domain semantic parsing in context with 4,298 question sequences. |
| Approach: | They present a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences. |
| Outcome: | The proposed dataset demonstrates that it has greater semantic diversity and can be generalized to unseen domains due to its cross-domain nature and the unseened databases at test time. |
DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models (2025.naacl-long)
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| Challenge: | Recent advances in video-text retrieval models have limited training data annotations. |
| Approach: | They propose a Video-Text Retrieval Paradigm with Relevance-based Augmentation which enhances video and text data using large foundation models to learn more generalized features. |
| Outcome: | The proposed method improves video-text retrieval performance over existing methods. |
Multimodal Pretraining for Dense Video Captioning (2020.aacl-main)
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| Challenge: | a billion hours of videos are being watched on YouTube every day . videos are difficult to skim through, making it harder to quickly target the relevant part(s) of a video. |
| Approach: | They propose to use a video timeline tag dataset to generate time-stamped annotations for videos . they propose to pretrain and finetune captioning models using YouCook2 and ViTT . |
| Outcome: | The proposed model generalizes well and is robust over a wide variety of instructional videos. |
SharPT: Shared Latent Space Prompt Tuning (2023.findings-eacl)
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| Challenge: | Prompt tuning is an efficient method for adapting large language models, but it is difficult and expensive to identify the source task that provides optimal prompts. |
| Approach: | They propose to learn a shared latent space which captures a set of basis skills from a mixture of source tasks and then transfer them to target tasks. |
| Outcome: | The proposed method outperforms previous methods on NLI, sentence completion, QA, conference resolution, word sense disambiguation and on various model scales. |
Emergence of Abstract State Representations in Embodied Sequence Modeling (2023.emnlp-main)
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| Challenge: | Sequence modeling is a simple yet versatile task that can be applied to more complex decision-making domains. |
| Approach: | They build a sequence modeling Transformer which takes a language instruction, actions, and environmental observations as inputs and then trains a model to reconstruct environmental layouts. |
| Outcome: | The proposed model can reconstruct environmental layouts from the inputs of the model and language instructions play a role in the reconstruction accuracy. |
Text Detoxification: Data Efficiency, Semantic Preservation and Model Generalization (2025.emnlp-main)
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| Challenge: | Existing methods for detoxification of text often rely on manually annotated data . xiangli: "detoxification of texts is a powerful way to remove toxic content" |
| Approach: | They propose a reinforcement learning framework that optimizes detoxification and semantic preservation without annotating large amounts of data. |
| Outcome: | The proposed method overcomes major limitations and surpasses humanannotated references across multiple benchmarks. |
Few-shot Unified Question Answering: Tuning Models or Prompts? (2023.findings-emnlp)
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| Challenge: | Question-answering (QA) tasks investigate specific question types, knowledge domains, or reasoning skills, leading to specialized models catering to specific categories of QA tasks. |
| Approach: | They propose to use model and prompt tuning for unified QA in a low-resource setting to overcome drawbacks of unified models. |
| Outcome: | The proposed model and prompt tuning paradigms outperform model tuning in a few-shot setting with a good initialization and achieve a significant performance boost from pre-training in 'low-resource' setting. |
Robust Transfer Learning with Pretrained Language Models through Adapters (2021.acl-short)
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| Challenge: | Existing approaches to transfer learning with pretrained transformer-based language models are not robust and can be adversarial. |
| Approach: | They propose a simple yet effective adapter-based approach to fine-tune language models on downstream tasks. |
| Outcome: | The proposed approach improves stability and adversarial robustness in transfer learning to various downstream tasks. |
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering (D19-1)
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| Challenge: | Object detection is used in vision and language tasks but is expensive to learn . popular models rely on annotating ground-truths for bounding boxes and semantic labels . empirically, object detection leads to effective transfer learning and improved captioning and visual question answering models . |
| Approach: | They examine the effect of decoupling box proposal and featurization on down-stream tasks . they propose a family of "two-stage" object detectors that propose category-agnostic bounding boxes . |
| Outcome: | The proposed method improves image captioning and visual question answering models by leveraging large amounts of labeled annotations. |
Long Document Summarization with Top-down and Bottom-up Inference (2023.findings-eacl)
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| Challenge: | Recent models infer latent representations of words or tokens with a transformer encoder, which is bottom-up and thus does not capture long-distance context well. |
| Approach: | They propose a method to infer latent representations of words or tokens in documents . they assume a hierarchical structure of a document where top-level captures long range dependency . |
| Outcome: | The proposed model can summarize an entire book and achieve competitive performance on a wide range of document summarization benchmarks. |
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases (D19-1)
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Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, Dragomir Radev
| Challenge: | CoSQL is a corpus for building cross-domain, general-purpose database querying dialogue systems. |
| Approach: | They present a corpus for building cross-domain, general-purpose database querying dialogue systems . they use a Wizard-of-Oz collection of 3k turns plus 10k+ annotated SQL queries . |
| Outcome: | The proposed corpus is based on a Wizard-of-Oz dataset of 3k dialogues querying 200 complex DBs spanning 138 domains. |
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models (2025.coling-main)
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Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang
| Challenge: | Existing large language models (LLMs) fail due to lack of knowledge or incorrect knowledge application. |
| Approach: | They propose a knowledge-augmented framework that constructs a formula set to provide explicit physics knowledge and utilizes checklists to guide effective knowledge application. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%. |
Neural-based Chinese Idiom Recommendation for Enhancing Elegance in Essay Writing (P19-1)
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| Challenge: | idiom recommendation is difficult because remembering idiomatic expressions is difficult, authors say . idiomas are often written in ancient classical Chinese for conciseness, meaning is difficult to remember . |
| Approach: | They propose to use a neural machine translation framework to recommend idioms . they assume that idiomatic expressions are written with one pseudo target language . |
| Outcome: | The proposed approach achieves promising performance compared with baseline methods. |