🧑‍🔬 Kaiqiang Song

🏛️ Affiliations

🎓University of Massachusetts AmherstAmherst Center · United States2026 · 1 papercurrent🏢TencentShenzhen · China2021–2024 · 9 papers🎓University of Central FloridaOrlando · United States2018–2023 · 9 papers🎓University of Southern CaliforniaLos Angeles · United States2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

🧭 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📅 Works by year

2020s

2010s

📄 Papers

The most cited first (OpenAlex and our library).

1. Scoring Sentence Singletons and Pairs for Abstractive Summarization (2019 · 157 citations)
   When people write summaries, they usually form each summary sentence by shortening one source sentence or by combining material from two.
2. InFoBench: Evaluating Instruction Following Ability in Large Language Models (2024 · 133 citations)
   Judging whether large language models obey user instructions usually relies on pairwise A/B comparisons, Elo ratings or a single holistic score.
3. MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning (2024 · 83 citations)
   General-purpose open-source large multimodal models (LMMs) handle natural photos well but struggle with charts. Charts encode data through abstract elements such as axes, legends and trend lines, and these models get little training on them. The authors address this with three contributions.

All works

5 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. Scoring Sentence Singletons and Pairs for Abstractive SummarizationAnnual Meeting of the Association for Computational Linguistics (ACL)Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, et al.2019 · 157 citations
2. InFoBench: Evaluating Instruction Following Ability in Large Language ModelsFindings of the Association for Computational LinguisticsYiwei Qin, Kaiqiang Song, Yebowen Hu, et al.2024 · 133 citations
3. MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction TuningConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, et al.2024 · 83 citations
4. Benchmarking and Improving Long-Text Translation with Large Language Models ↗Findings of the Association for Computational Linguistics: ACL 2024 · no summary yetLongyue Wang et al.2024 · 26 citations
5. Skills-in-Context Prompting: Unlocking Compositionality in Large Language Models ↗arXiv:2308.00304 · no summary yetJiaao Chen et al.2023 · 16 citations

🤝 Co-authors here

12 of 14 co-authors with a page, the most papers together first.

• Dong Yu ×2
• Fei Liu ×2
• Sangwoo Cho ×2
• Wenlin Yao ×2
• Xiaoyang Wang ×2
• Doo Soon Kim
• Franck Dernoncourt
• Fuxiao Liu
• Jianshu Chen
• Logan Lebanoff
• Pengfei Liu
• Seokhwan Kim

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2019
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2024
• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2024
• Findings of the Association for Computational Linguistics: ACL 2024

🔗 Cited by

Papers of our library cite their works 253 times (of the 141,840 papers whose bibliographies we read); 15 citing papers with a page here →.

Updated 24 October 2026

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