6 citations · 8 across the 37 of their papers we have counts for
10 papers · 1 filter
REWIND: Uncertainty-Guided Trajectory Repair for Test-Time Reasoning
Yi Ding, Mengyu Wang, Min Zhou +1
An early mistake in the generated reasoning can constrain all subsequent reasoning. Repeated-generation methods, like MCMC-based approaches, attempt to improve output quality by sa…
Self Knowledge Re-expression: A Fully Local Method for Adapting LLMs to Tasks Using Intrinsic Knowledge
Mengyu Wang, Xiaoying Zhi, Zhiyi Li +4
While the next-token prediction (NTP) paradigm enables large language models (LLMs) to express their intrinsic knowledge, its sequential nature constrains performance on specialize…
MedConclusion: A Benchmark for Biomedical Conclusion Generation from Structured Abstracts
Weiyue Li, Ruizhi Qian, Yi Li +5
Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific conclusions from structured bio…
Individual and Combined Effects of English as a Second Language and Typos on LLM Performance
Serena Liu, Yutong Yang, Prisha Sheth +9
Large language models (LLMs) are used globally, and because much of their training data is in English, they typically perform best on English inputs. As a result, many non-native E…
TrailBlazer: History-Guided Reinforcement Learning for Black-Box LLM Jailbreaking
Sung-Hoon Yoon, Ruizhi Qian, Minda Zhao +2
Large Language Models (LLMs) have become integral to many domains, making their safety a critical priority. Prior jailbreaking research has explored diverse approaches, including p…
LLM Review: Enhancing Creative Writing via Blind Peer Review Feedback
Weiyue Li, Mingxiao Song, Zhenda Shen +6
Large Language Models (LLMs) often struggle with creative generation, and multi-agent frameworks that improve reasoning through interaction can paradoxically hinder creativity by i…
Grading Scale Impact on LLM-as-a-Judge: Human-LLM Alignment Is Highest on 0-5 Grading Scale
Weiyue Li, Minda Zhao, Weixuan Dong +12
Large language models (LLMs) are increasingly used as automated evaluators, yet prior works demonstrate that these LLM judges often lack consistency in scoring when the prompt is a…
Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions
Minda Zhao, Yilun Du, Mengyu Wang
As large language models (LLMs) transition from chat interfaces to integral components of stochastic pipelines and systems approaching general intelligence, the ability to faithful…
One More Question is Enough, Expert Question Decomposition (EQD) Model for Domain Quantitative Reasoning
Mengyu Wang, Sotirios Sabanis, Miguel de Carvalho +2
Domain-specific quantitative reasoning remains a major challenge for large language models (LLMs), especially in fields requiring expert knowledge and complex question answering (Q…
Transformation of Dense and Sparse Text Representations
Wenpeng Hu, Mengyu Wang, Bing Liu +5
Sparsity is regarded as a desirable property of representations, especially in terms of explanation. However, its usage has been limited due to the gap with dense representations.…