🧑‍🔬 Kai Xiong

🏛️ Affiliations

🎓Harbin Institute of TechnologyHarbin · China2026 · 3 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate (2023 · 94 citations)
   Work on LLM consistency usually examines a single model. This paper asks whether several LLMs can work together and agree on a shared answer. The authors use multi-choice commonsense reasoning as a testbed because disagreement between models can be measured exactly. They define a metric, INCON, which is the share of…
2. MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward Optimization (2026 · 1 citation)
   GRPO aligns LLMs well when answers can be checked automatically, as in math or code. Open-ended generation has no such check, and its goals often conflict, for example creativity versus factuality. Existing fixes either rely on costly LLM judges or combine cheap heuristic rewards (perplexity, entropy, format) using…
3. MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models (2026)
   Most existing causal benchmarks for LLMs use everyday commonsense scenarios with short, single-hop cause–effect links.

All works

6 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. Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via DebateFindings of the Association for Computational LinguisticsKai Xiong, Xiao Ding, Yixin Cao, et al.2023 · 94 citations
2. MAESTRO: Meta-learning Adaptive Estimation of Scalarization Trade-offs for Reward OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Yang Zhao, Hepeng Wang, Xiao Ding, et al.2026 · 1 citation
3. MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language ModelsFindings of the Association for Computational LinguisticsPeng Wang, Yuxiong Yan, Xiao Ding, et al.2026
4. PuzzleClone: A DSL-Powered Framework for Synthesizing Verifiable Data ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetKai Xiong et al.2026
5. Is EEG-to-Text Feasible in Real-World Scenarios? An In-Depth Analysis Using a Neuropsychology-Inspired Benchmark ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZihan Zhang et al.2026
6. Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYang Zhao et al.2026

🤝 Co-authors here

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

• Bing Qin ×3
• Ting Liu ×3
• Xiao Ding ×3
• Bibo Cai ×2
• Chao Peng
• Dandan Tu
• Li Du
• Peng Wang
• Yang Zhao
• Yixin Cao
• Yutai Hou
• Zhouhao Sun

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) ×3: ACL 2026
• Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026, Findings of the Association for Computational Linguistics 2023
• Findings of the Association for Computational Linguistics: ACL 2026

🔗 Cited by

Papers of our library cite their works 54 times (of the 149,439 papers whose bibliographies we read); 10 citing papers with a page here →.

Updated 1 November 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/kai-xiong/