--- title: "Danqi Chen — papers, page 2 · Princeton University" description: "Danqi Chen, Princeton University: 34 papers on Elamur Research (2013–2025), h-index 45 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/danqi-chen/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/danqi-chen/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Danqi Chen](https://md.research.elamur.ai/free/en/authors/danqi-chen/) / page 2 # Danqi Chen · page 2 34 papers in our library · 2013–2025 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/danqi-chen/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/danqi-chen/) [1](https://md.research.elamur.ai/free/en/authors/danqi-chen/) **2** 26. **[Open-Domain Question Answering](https://md.research.elamur.ai/free/en/papers/chen-2020-open-domain-question-answering/)** *(2020 · 129 citations)* This is an ACL 2020 tutorial abstract, not an experimental paper. It reports no new results. Its contribution is a structured overview of open-domain QA, the task of answering arbitrary questions using a very large document collection that covers many topics. 27. **[Learning Dense Representations of Phrases at Scale](https://md.research.elamur.ai/free/en/papers/lee-2021-learning-dense-representations-phrases-scale/)** *(2021 · 126 citations)* DensePhrases tackles open-domain question answering as retrieval over a pre-built index of phrases. It does not run a reader model over retrieved documents at query time. Earlier phrase-retrieval systems (DenSPI, DenSPI + Sparc) relied on sparse vectors and still trailed retriever-reader pipelines. The authors ask… 28. **[What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning](https://md.research.elamur.ai/free/en/papers/pan-2023-what-context-learning-learns-context/)** *(2023 · 102 citations)* The paper asks what language models actually do when they solve tasks from a few in-context examples. Do they simply identify a task they already know from pre-training, or do they acquire new input-label associations from the prompt? The authors separate these into two abilities. Task recognition (TR) means… 29. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/)** *(2025 · 89 citations)* The paper asks how to turn a short-context pre-trained LM into a strong long-context model through continued pre-training followed by SFT. 30. **[Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving](https://md.research.elamur.ai/free/en/papers/lin-2025-goedel-prover-frontier-model-open/)** *(2025 · 84 citations)* Training language models to write machine-checkable proofs is hard because very little mathematics exists in formal form. 31. **[QuRating: Selecting High-Quality Data for Training Language Models](https://md.research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/)** *(2024 · 74 citations)* Which documents a language model is pre-trained on strongly shapes its abilities. Yet common filters, such as C4/Gopher-style rules, perplexity filtering, or resemblance to reference domains like Wikipedia, capture quality only roughly. QuRating turns intuitive human notions of quality into scores along four axes… 32. **[Reliable, Adaptable, and Attributable Language Models with Retrieval](https://md.research.elamur.ai/free/en/papers/asai-2024-reliable-adaptable-attributable-language-models/)** *(2024 · 45 citations)* This position paper by Asai et al. contends that retrieval-augmented LMs should succeed purely parametric LMs as the main design for future language models. It contains no new experiments, datasets, or models. Instead, it surveys prior work and lists five weaknesses of parametric models: hallucinated facts, outputs… 33. **[Long-Context Language Modeling with Parallel Context Encoding](https://md.research.elamur.ai/free/en/papers/yen-2024-long-context-language-modeling-parallel/)** *(2024 · 44 citations)* Decoder-only LLMs struggle with long inputs for three reasons. Positional encodings do not extrapolate well past the training length, full attention becomes expensive, and long instruction data is scarce. The authors propose CEPE (Context Expansion with Parallel Encoding), which attaches two trainable parts to a… 34. **[LitSearch: A Retrieval Benchmark for Scientific Literature Search](https://md.research.elamur.ai/free/en/papers/ajith-2024-litsearch-retrieval-benchmark-scientific-literature/)** *(2024 · 39 citations)* Researchers often look for papers that match a precise description, such as a particular method or setting. 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