3 citations · 4 across the 10 of their papers we have counts for
8 papers · 1 filter
The Window Is in the Reasoning: When LLM Agents Decide to Follow Prompt Injections
Haoxuan Jia, Yang Liu, Bin Chong +13
LLM agents that read untrusted content can be hijacked by prompt injections. Runtime monitors that try to catch a hijack before the agent acts, and benchmarks that score each task…
Hindsight Memory-PRM: Supervising Memory Management with Auditable Hindsight Credit
Haoxuan Jia, Yang Liu, Yingguang Yang +14
Memory operations of long-horizon LLM agents are hard to supervise: an operation's value is unobservable when it is taken. But they are special -- they leave machine-readable evide…
Characterizing Rhetorical Misalignment in Decision-Making with Language Models
Zirui Cheng, Joey Chan, Simo Du +3
Human decision-making is often shaped by a range of well-documented cognitive biases. As large language models (LLMs) become increasingly integrated into high-stakes human-AI decis…
FinHarness: An Inline Lifecycle Safety Harness for Finance LLM Agents
Haoxuan Jia, Yang Liu, Bin Chong +10
Finance LLM agents must simultaneously block prompt-induced unauthorized actions and approve legitimate multi-step business workflows. However, boundary filters often miss irrevers…
ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies
Shang Luo, Yingguang Yang, Zhenchen Sun +8
The democratization of LLMs has accelerated the generation and circulation of highly fluent disinformation, making traditional syntax-semantic verification increasingly insufficien…
Source-Aware Training Enables Knowledge Attribution in Language Models
Muhammad Khalifa, David Wadden, Emma Strubell +4
Large language models (LLMs) learn a vast amount of knowledge during pretraining, but they are often oblivious to the source(s) of such knowledge. We investigate the problem of int…
Catwalk: A Unified Language Model Evaluation Framework for Many Datasets
Dirk Groeneveld, Anas Awadalla, Iz Beltagy +7
The success of large language models has shifted the evaluation paradigms in natural language processing (NLP). The community's interest has drifted towards comparing NLP models ac…
Language Models Hallucinate, but May Excel at Fact Verification
Jian Guan, Jesse Dodge, David Wadden +2
Recent progress in natural language processing (NLP) owes much to remarkable advances in large language models (LLMs). Nevertheless, LLMs frequently "hallucinate," resulting in non…