Papers by Shichao Sun
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. |
Contrastive Preference Learning for Neural Machine Translation (2024.findings-naacl)
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| Challenge: | Existing discrepancies between token-level objective and overall sequence-level quality of a model are causing exposure bias and other issues in NMT. |
| Approach: | They propose a contrastive preference model that integrates an indicator function to fine-tune a pre-trained model in Neural Machine Translation. |
| Outcome: | The proposed model outperforms the traditional Plackett-Luce model on three language pairs and also outperFORMs token-level and sequence-level baseline models. |
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. |
Dissecting Human and LLM Preferences (2024.acl-long)
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| Challenge: | a recent study shows that human and Large Language Model preferences are important for model fine-tuning and evaluation. |
| Approach: | They dissect the preferences of human and 32 different Large Language Models to understand their quantitative composition. |
| Outcome: | The proposed model is compared with 32 different large language models using real-world user-model conversations. |
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. |
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 . |
FRoG: Evaluating Fuzzy Reasoning of Generalized Quantifiers in LLMs (2024.emnlp-main)
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| Challenge: | Existing methods to enhance reasoning do not consistently improve performance in tasks involving fuzzy logic. |
| Approach: | They propose a benchmark for fuzzy reasoning that incorporates generalized quantifiers. |
| Outcome: | The proposed benchmark shows that existing methods do not improve on FRoG . strong mathematical reasoning skills are not indicative of success, the authors show . |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
The Critique of Critique (2024.findings-acl)
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| Challenge: | MetaCritique builds specific quantification criteria to evaluate the quality of critique . a systematic method to evaluate critique is lacking. |
| Approach: | They propose a critique of critique, termed MetaCritique, which builds specific quantification criteria and aggregates each AIU's judgment for the overall score. |
| Outcome: | The proposed method can achieve near-human performance across 16 datasets. |