--- title: "William Yang Wang — papers, page 2 — Elamur Research" description: "William Yang Wang: 35 papers on Elamur Research (2012–2026), h-index 64 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/william-yang-wang/page/2/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/william-yang-wang/page/2/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [William Yang Wang](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) / page 2 # William Yang Wang · page 2 35 papers in our library · 2012–2026 · page 2 of 2 [The profile, affiliations and co-authors →](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) ## Papers The most cited first (OpenAlex and our library). [← prev](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) [1](https://md.research.elamur.ai/free/en/authors/william-yang-wang/) **2** 26. **[RuleArena: A Benchmark for Rule-Guided Reasoning with LLMs in Real-World Scenarios](https://md.research.elamur.ai/free/en/papers/zhou-2025-rulearena-benchmark-rule-guided-reasoning/)** *(2025 · 21 citations)* Benchmarks of LLM instruction-following mostly check style constraints such as format or length, or they test simple first-order logic. RuleArena instead tests whether models can reason under real regulations written in natural language. The authors gathered 95 rules from three domains: 27. **[ALGO: Synthesizing Algorithmic Programs with LLM-Generated Oracle Verifiers](https://md.research.elamur.ai/free/en/papers/zhang-2023-algo-synthesizing-algorithmic-programs-llm/)** *(2023 · 19 citations)* LLMs handle code generation well when a task spells out exactly what to implement. They do much worse on competitive-programming problems, where the model must first choose an efficient algorithm, and their output still needs human checking. ALGO tackles both issues. It builds on an observation: writing a slow… 28. **[Aristotle: Mastering Logical Reasoning with A Logic-Complete Decompose-Search-Resolve Framework](https://md.research.elamur.ai/free/en/papers/xu-2025-aristotle-mastering-logical-reasoning-logic/)** *(2025 · 16 citations)* Large language models still struggle with logical reasoning. General search-based methods such as ToT and GoT ignore the formal structure of logic problems: they split tasks along surface wording, depend on unreliable LLM evaluators to choose paths, and make logical slips when solving sub-steps. The authors present… 29. **[AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge](https://md.research.elamur.ai/free/en/papers/wu-2025-antileakbench-preventing-data-contamination-automatically/)** *(2025 · 12 citations)* Large language models are usually tested on public, static benchmarks, and those test items can leak into the training data of later models, which inflates their scores. 30. **[PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers](https://md.research.elamur.ai/free/en/papers/saxon-2023-peco-examining-single-sentence-label/)** *(2023 · 9 citations)* NLI is meant to be a pairwise task: the relation between premise and hypothesis should depend on both sentences together. 31. **[Simple Models for Word Formation in English Slang](https://md.research.elamur.ai/free/en/papers/kulkarni-2018-simple-models-word-formation-english/)** *(2018 · 8 citations)* The paper models three kinds of irregular word formation in English slang: blends (such as "fantabulous" from "fantastic" and "fabulous"), clippings (such as "prof" from "professor"), and reduplicatives (such as "boo-boo"). 32. **[“Love ya, jerkface”: Using Sparse Log-Linear Models to Build Positive and Impolite Relationships with Teens](https://md.research.elamur.ai/free/en/papers/wang-2012-love-ya-jerkface-sparse-log/)** *(2012 · 7 citations)* The paper asks how a tutoring dialogue system could read and signal rapport with teenagers, given that closeness shows up through both praise and friendly rudeness. 33. **[Detecting Training Data of Large Language Models via Expectation Maximization](https://md.research.elamur.ai/free/en/papers/kim-2026-detecting-training-data-large-language/)** *(2026 · 5 citations)* This paper tackles membership inference for large language models: deciding whether a given text was part of a model's pre-training data. 34. **[InductionBench: LLMs Fail in the Simplest Complexity Class](https://md.research.elamur.ai/free/en/papers/hua-2025-inductionbench-llms-fail-simplest-complexity/)** *(2025 · 5 citations)* Most reasoning benchmarks for LLMs test deduction: the rules are given (axioms, programming syntax) and must be applied. 35. **[WildSci: Advancing Scientific Reasoning from In-the-Wild Literature](https://md.research.elamur.ai/free/en/papers/liu-2026-wildsci-advancing-scientific-reasoning-wild/)** *(2026)* Reinforcement learning with verifiable rewards (RLVR) has driven LLM reasoning gains mostly in math and code. 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