Xinghua Zhang
Xinghua Zhang has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Question answering; often with Haiyang Yu, Bowen Yu, Fei Huang.
OpenAlex · ORCID · Wikidata · h-index 15 · 109 works · 773 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 119 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4879
Affiliations
Concepts, shelves and topics
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workQuestion answering · 1 work
Works by year
2020s
Papers
- Wider and Deeper LLM Networks are Fairer LLM Evaluators (2023 · 74 citations)
Judging open-ended LLM outputs against human preference is difficult. Lexical metrics such as BLEU and ROUGE track human opinion poorly, and manual annotation is slow and expensive. Existing LLM-as-evaluator schemes such as FairEval and LLM-as-a-judge mostly average several identical LLM judgments. This amounts to a… - Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QA (2024 · 71 citations)
Most current long-context benchmarks make inputs longer by padding them with unrelated filler text. In those setups, the evidence for an answer usually sits in one place, so a model can skip most of the input and still answer correctly. Wang et al. address this with Loong, a multi-document QA benchmark where every…
All works
- Wider and Deeper LLM Networks are Fairer LLM EvaluatorsarXiv:2308.01862Xinghua Zhang, Bowen Yu, Haiyang Yu, et al.2023 · 74 citations
- Leave No Document Behind: Benchmarking Long-Context LLMs with Extended Multi-Doc QAConference on Empirical Methods in Natural Language Processing (EMNLP)Minzheng Wang, Longze Chen, Fu Cheng, et al.2024 · 71 citations
- EIFBENCH: Extremely Complex Instruction Following Benchmark for Large Language ModelsProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · original ↗ · no summary yetTao Zou et al.2025 · 5 citations
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