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Bolin Ding

Bolin Ding has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Jinyang Gao, Shuchang Tao, Aiwei Liu.

4 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 47 · 249 works · 9,137 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 145 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3946

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 11 more institutions on single bylines are not listed.

 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. Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation (2023 · 463 citations)
    Large language models have become a strong option for turning natural-language questions into SQL. However, no one had systematically compared the many prompting choices behind these systems. Gao et al. address this with a broad benchmark on Spider. They test how the database and question are written into the prompt…
  2. Language Adaptation of Large Language Models: An Empirical Study on LLaMA2 (2025 · 7 citations)
    Most large language models learn mainly from English text and handle other languages less well. Building a balanced multilingual model from scratch is too costly for most groups, so adapting an existing model is the practical alternative. Methods for transferring older models such as BERT across languages may not…
  3. d-TreeRPO: Towards More Reliable Policy Optimization for Diffusion Language Models (2026 · 3 citations)
    Reinforcement learning for diffusion large language models (dLLMs) has two weak spots. Rewards are usually sparse outcome scores or unverified process signals, and per-token log-probabilities are approximated without accounting for the expectation over all possible decoding orders. d-TreeRPO addresses both. It…
  4. Incentivizing Strong Reasoning from Weak Supervision (2026 · 1 citation)
    Improving LLM reasoning usually requires either reinforcement learning with verifiable rewards, which is compute-intensive, or SFT on long chain-of-thought (CoT) data from very strong teachers, which is costly to obtain.

All works

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

  1. SoK: Privacy-Preserving Data Synthesis ↗Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · no summary yetYuzheng Hu et al.2024 · 30 citations

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 145 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

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