Large Language Model
Understanding Bias in Large-Scale Visual Datasets
However, the concrete forms of bias among these datasets remain unclear.
Opponent Modeling with In-context Search
Opponent modeling is a longstanding research topic aimed at enhancing decision-making by modeling information about opponents in multi-agent environments. However, existing approaches often face challenges such as having difficulty generalizing to unknown opponent policies and conducting unstable performance.
The Fine-Grained Complexity of Gradient Computation for Training Large Language Models
To train an LLM, one needs to alternatingly run'forward' computations and'backward' computations.
Self-Supervised Alignment with Mutual Information Learning to Follow Principles without Preference Labels
Aligning LMs to human preferences can be resource-intensive and technically challenging.
Tree of Attacks: Jailbreaking Black-Box LLMs Automatically
While Large Language Models (LLMs) display versatile functionality, they continue to generate harmful, biased, and toxic content, as demonstrated by the prevalence of human-designed jailbreaks .
TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
This lack of rich textual edge annotations significantly limits the exploration of contextual relationships between entities, hindering deeper insights into graph-structured data.
HW-Aware-GPT-Bench
This energy usage is comparable to that of around 180,000 U.S. households, each