Papers by Ziqiang Cao
Data Selection Curriculum for Abstractive Text Summarization (2023.findings-emnlp)
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| Challenge: | Abstractive Text Summarization (ATS) models are commonly trained using large-scale data that is randomly shuffled. |
| Approach: | They propose a data selection curriculum scoring system that measures the learning difficulty of an ATS model and expected performance on an instance. |
| Outcome: | The proposed system surpasses baselines on CNN/DailyMail dataset, utilizing 20% of available instances. |
Few-shot Query-Focused Summarization with Prefix-Merging (2022.emnlp-main)
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| Challenge: | Query-focused summarization has been considered as an important extension for text summarizing . lack of large-scale datasets hinders its development . |
| Approach: | They propose to integrate text summarization and question answering into a prefix-based pretraining strategy for few-shot learning in query-focused summarizing. |
| Outcome: | The proposed prefix-based pretraining outperforms fine-tuning on query-focused summarization. |
Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)
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| Challenge: | Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm. |
| Approach: | They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency. |
| Outcome: | The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks. |
Personalized Large Language Model Assistant with Evolving Conditional Memory (2025.coling-main)
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| Challenge: | With the rapid development of large language models, personalized large language model assistants like ChatGPT are limited in personalized services. |
| Approach: | They propose a plug-and-play framework that could facilitate personalized large language model assistants with evolving conditional memory. |
| Outcome: | The proposed framework can preserve the knowledge and experience from the history dialogue with the user, which can be applied to future tailored responses that better align with the users' preferences. |
Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)
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| Challenge: | Existing models for text generation use a discrete data embedding module to map the data into the continuous space. |
| Approach: | They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space. |
| Outcome: | The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks. |
UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation (2025.acl-long)
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| Challenge: | Existing methods to improve reasoning abilities of Large Language Models (LLMs) have limitations due to excessive growth in context length, causing large hardware burden. |
| Approach: | They propose a novel Unified ICL framework that unifies demonstration compression, demonstration selection, and final response generation. |
| Outcome: | The proposed framework unifies demonstration compression, demonstration selection, and final response generation. |
Separating Context and Pattern: Learning Disentangled Sentence Representations for Low-Resource Extractive Summarization (2023.findings-acl)
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| Challenge: | Context information is one of the key factors for extractive summarization, but other factors can be used to identify sentence importance. |
| Approach: | They propose to disentangle context and pattern factors for extractive summarization . they separate context and patterns for a better generalization ability in low-resource setting . |
| Outcome: | The proposed model can be used in the zero-shot setting or fine-tuned in the few-shot settings. |
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness (2022.findings-emnlp)
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| Challenge: | Existing models of abstractive summarization are able to generate fluent and coherent summaries, but they still suffer from the unfaithful generation problem. |
| Approach: | They propose to improve the faithfulness of existing models by enhancing their factual robustness by using a novel training strategy, namely FRSUM, which teaches the model to defend against both explicit adversarial samples and implicit factual adversarials. |
| Outcome: | The proposed training strategy improves faithfulness of various models, such as T5, BART, and T5 . |
Prompt Chaining or Stepwise Prompt? Refinement in Text Summarization (2024.findings-acl)
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| Challenge: | Large language models (LLMs) can improve summary quality by mirroring a human-like iterative process of critique and refinement starting from the initial draft. |
| Approach: | They propose to use Prompt Chaining and Stepwise Prompting to perform iterative refinement . they propose to combine the two methods to produce a more favorable outcome . |
| Outcome: | The proposed methods can improve summary quality by mirroring a human-like iterative process . the results show that the prompt chaining method produces a more favorable outcome . |
Diffusion Language Model with Query-Document Relevance for Query-Focused Summarization (2023.findings-emnlp)
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| Challenge: | Query-Focused Summarization (QFS) aims to generate summaries that address specific queries by extracting crucial information from source documents. |
| Approach: | They propose a non-autoregressive diffusion language model that incorporates query-document fragment relevance and query-doctoral global relevance to enhance the adaptability of QFS tasks. |
| Outcome: | The proposed model achieves state-of-the-art performance on Debatepedia and PubMedQA datasets in ROUGE scores, GPT-4, and human evaluations. |
The Bidirectional Process Reward Model (2026.acl-long)
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| Challenge: | Process reward models (PRMs) assign fine-grained scores to intermediate reasoning steps within a solution trajectory. |
| Approach: | They propose a bidirectional evaluation paradigm that integrates a parallel evaluation stream alongside the L2R evaluation scheme and a gating mechanism to fuse the reward scores. |
| Outcome: | The proposed model surpasses unidirectional baselines in multiple benchmarks, LLM objectives and sampling policies. |
Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization (P18-1)
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| Challenge: | Existing summarization systems rely on the source text to generate summaries, which tends to work unstably. |
| Approach: | They propose to use existing summaries as soft templates to guide the seq2seq model . they use a popular IR platform to Retrieve proper summary as candidate templates . |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of informativeness and readability. |
Interleaved Tool-Call Reasoning for Protein Function Understanding (2026.acl-long)
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| Challenge: | Recent advances in large language models have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. |
| Approach: | They propose a tool-augmented protein reasoning agent that unifies problem decomposition, tool invocation, and grounded answer generation. |
| Outcome: | The proposed protein function understanding agent outperforms text-only reasoning models with an average performance improvement of 103%. |
Improving Copy-oriented Text Generation via EDU Copy Mechanism (2024.lrec-main)
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| Challenge: | Existing extractive models generate texts through word-by-word decoding, causing factual inconsistencies and slow inference. |
| Approach: | They propose a framework that integrates the behavior of copying EDUs into generative models. |
| Outcome: | The proposed framework reduces the number of generated tokens significantly. |
KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)
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| Challenge: | Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability. |
| Approach: | They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach. |
| Outcome: | The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios. |
BASS: Boosting Abstractive Summarization with Unified Semantic Graph (2021.acl-long)
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| Challenge: | Abstractive summarization for long-document or multi-document remains challenging for Seq2Seq as it does not analyze long-distance relations in text. |
| Approach: | They propose a framework for Boosting Abstractive Summarization based on a unified Semantic graph which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. |
| Outcome: | The proposed framework improves document representation and summary generation process by leveraging the graph structure. |
CoUDA: Coherence Evaluation via Unified Data Augmentation (2024.naacl-long)
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| Challenge: | Existing data augmentations for coherence evaluation rely on heuristic rules and lack designing criteria. |
| Approach: | They propose a data augmentation framework that breaks down coherence into global and local aspects and designs augmentation strategies for both aspects. |
| Outcome: | The proposed framework surpasses recent models in scoring and ranking tasks with 233M parameters. |