2 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
Implicit Reasoning Steering via Concept Chaining
Xiao Ye, Sanika Chavan, Yuxi Huang +4
Large language models often appear to reason reliably, yet on many questions repeated sampling yields both correct and incorrect answers, revealing an underlying fragility in how f…
ArenaBencher: Automatic Benchmark Evolution via Multi-Model Competitive Evaluation
Qin Liu, Jacob Dineen, Yuxi Huang +4
Benchmarks are central to measuring the capabilities of large language models and guiding model development, yet widespread data leakage from pretraining corpora undermines their v…
Familiarity-Aware Evidence Compression for Retrieval-Augmented Generation
Dongwon Jung, Qin Liu, Tenghao Huang +2
Retrieval-augmented generation (RAG) improves large language models (LMs) by incorporating non-parametric knowledge through evidence retrieved from external sources. However, it of…
FamiCom: Further Demystifying Prompts for Language Models with Task-Agnostic Performance Estimation
Bangzheng Li, Ben Zhou, Xingyu Fu +3
Language models have shown impressive in-context-learning capabilities, which allow them to benefit from input prompts and perform better on downstream end tasks. Existing works in…
Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking
Nan Xu, Fei Wang, Ben Zhou +3
While large language models (LLMs) have demonstrated increasing power, they have also given rise to a wide range of harmful behaviors. As representatives, jailbreak attacks can pro…
Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
Bangzheng Li, Ben Zhou, Fei Wang +3
Despite the recent advancement in large language models (LLMs) and their high performances across numerous benchmarks, recent research has unveiled that LLMs suffer from hallucinat…