Quan Shi
Quan Shi has 2 papers in our library, in 2024, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Michael Tang, Hongjin Su, Howard Yen.
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📄 2 works in our library · 🔗 Cited 98 times in our library · ⭐ Paper Stars ranking: #7833
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksInformation Systems · 2 worksLanguage and Linguistics · 2 worksComputer Science Applications · 1 work
🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 1 workEvaluation & benchmarks · 1 workInformation retrieval & search · 1 workReasoning · 1 work
💡 Concepts 1Key ideas and methods explained on our concept pages.Inverted index · 1 work
Works by year
2020s
Papers
- BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval (2024 · 77 citations)
Standard retrieval benchmarks such as BEIR and MTEB mostly test search-engine-style questions. In these, relevant documents share many words or meanings with the query. Many real needs are harder. A programmer may need documentation for a function that is never named in the question, or a student may need a problem… - Can Language Models Solve Olympiad Programming? (2024 · 28 citations)
Widely used code benchmarks such as HumanEval and MBPP are now nearly saturated, so they no longer show where language models fall short in algorithmic reasoning.
Co-authors here
- Michael Tang ×2
- Hongjin Su
- Howard Yen
- Jinsung Yoon
- Karthik Narasimhan
- Mengzhou Xia
- Niklas Muennighoff
- Ruoxi Sun
- Shunyu Yao
- Weijia Shi
- Zachary S. Siegel
Cited by
Updated
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