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Weitao Ma

Weitao Ma has 6 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Xiaocheng Feng, Bing Qin, Xiachong Feng.

6 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 5 · 46 works · 2,418 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 862 times in our library · ⭐ Paper Stars ranking: #1077

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

🎓Harbin Institute of TechnologyHarbin · China2023–2026 · 43 paperscurrent🏢MeituanBeijing · 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.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 6 worksComputer Science · 6 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 6 worksInformation Systems · 3 worksCognitive Neuroscience · 1 workNeuroscience · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 4 worksInformation retrieval & search · 1 workReasoning · 1 work

💡 Concepts 3Key ideas and methods explained on our concept pages.Chatbots · 1 workLanguage model · 1 workLarge language models · 1 work

 Works by year

2020s

20264 papers20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions (2023 · 3,231 citations)
    Large language models often produce fluent, convincing text that is not supported by facts or by the input they were given.
  2. Improving Contextual Faithfulness of Large Language Models via Retrieval Heads-Induced Optimization (2025 · 11 citations)
    The paper addresses faithfulness hallucination in retrieval-augmented long-form question answering (LFQA), where a model writes paragraph-length answers containing claims that the retrieved documents do not support or that contradict them.
  3. x1: Learning to Think Adaptively Across Languages and Cultures (2026 · 1 citation)
    Most reasoning LLMs think mainly in one dominant language, usually English, even when a question is posed in another language or depends on a specific culture.
  4. Adaptive Backtracking for Privacy Protection in Large Language Models (2026)
    In retrieval-augmented generation, attackers can write prompts that make an LLM reveal private details from the documents it retrieves.
  5. Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026)
    LLM agents working on long-horizon tasks need a memory system that turns a stream of incoming text chunks into a compact, usable store.
  6. ImplicitMemBench: Measuring Unconscious Behavioral Adaptation in Large Language Models (2026)
    Existing memory benchmarks for LLM agents, such as LoCoMo, LongMemEval and MemoryAgentBench, test whether a model can deliberately retrieve stored facts when asked.

 Co-authors here

12 of 28 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 862 times (of the 149,439 papers whose bibliographies we read); 207 citing papers with a page here →.

Updated

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