SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning (2024 · 85 citations)
SeaEval is a benchmark for multilingual foundation models. It addresses gaps that monolingual and bilingual test suites leave open, namely cultural comprehension and whether a model's knowledge stays the same across languages. The benchmark covers four capability areas: classic NLP tasks, complex reasoning, cultural…
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing (2024 · 31 citations)
Planning-style reasoning with LLMs, such as reasoning-as-planning with MCTS, makes rationales more reliable but is slow at inference because every intermediate state must be scored during search.
Preference Optimization for Reasoning with Pseudo Feedback (2024 · 15 citations)
Preference learning methods such as DPO and PPO help LLMs reason better in math and coding. However, they need reliable labels to separate good solutions from bad ones, and expert-verified reasoning data is scarce and costly to scale. The authors propose PFPO (Pseudo-Feedback Preference Optimization), which treats…
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks (2025 · 13 citations)
LLMs that combine chain-of-thought with retrieval-augmented generation often fail on hard, knowledge-heavy problems such as competitive programming.

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

Retrieving Multimodal Information for Augmented Generation: A Survey ↗Findings of the Association for Computational Linguistics: EMNLP 2023 · no summary yetRuochen Zhao et al.2023 · 96 citations
SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural ReasoningConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Bin Wang, Zhengyuan Liu, Xin Huang, et al.2024 · 85 citations
A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems ↗arXiv:2504.09037 · no summary yetZixuan Ke et al.2025 · 39 citations
Learning Planning-based Reasoning by Trajectories Collection and Process Reward SynthesizingConference on Empirical Methods in Natural Language Processing (EMNLP)Fangkai Jiao, Chengwei Qin, Zhengyuan Liu, et al.2024 · 31 citations
Preference Optimization for Reasoning with Pseudo FeedbackarXiv:2411.16345Fangkai Jiao, Geyang Guo, Xingxing Zhang, et al.2024 · 15 citations
Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging TasksAnnual Meeting of the Association for Computational Linguistics (ACL)Xingxuan Li, Weiwen Xu, Ruochen Zhao, et al.2025 · 13 citations

Co-authors here

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

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

Papers of our library cite their works 195 times (of the 149,439 papers whose bibliographies we read); 40 citing papers with a page here →.

Updated 30 October 2026