4 citations · 10 across the 9 of their papers we have counts for
9 papers
Local Causal Attribution of Chain-of-Thought Reasoning
Dennis Wei, Yannis Belkhiter, Erik Miehling +1
Understanding the causal structure of a language model's thought process is a problem of significant importance for both transparency and safety. In this work, we take a local appr…
Multi-component Causal Tracing in Large Language Models
Zirui Yan, Dennis Wei, Dmitriy A. Katz +2
Causal tracing systematically intervenes on a large language model's (LLM's) internal representations to uncover and quantify the causal pathways linking specific inputs or computa…
AI Steerability 360: A Toolkit for Steering Large Language Models
Erik Miehling, Karthikeyan Natesan Ramamurthy, Praveen Venkateswaran +10
The AI Steerability 360 toolkit is an extensible, open-source Python library for steering LLMs. Steering abstractions are designed around four model control surfaces: input (modifi…
Entropy-Aware On-Policy Distillation of Language Models
Woogyeol Jin, Taywon Min, Yongjin Yang +5
On-policy distillation is a promising approach for transferring knowledge between language models, where a student learns from dense token-level signals along its own trajectories.…
Highlight All the Phrases: Enhancing LLM Transparency through Visual Factuality Indicators
Hyo Jin Do, Rachel Ostrand, Werner Geyer +3
Large language models (LLMs) are susceptible to generating inaccurate or false information, often referred to as "hallucinations" or "confabulations." While several technical advan…
Facilitating Human-LLM Collaboration through Factuality Scores and Source Attributions
Hyo Jin Do, Rachel Ostrand, Justin D. Weisz +5
While humans increasingly rely on large language models (LLMs), they are susceptible to generating inaccurate or false information, also known as "hallucinations". Technical advanc…
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers
Hussein Mozannar, Valerie Chen, Mohammed Alsobay +7
Evaluation of large language models for code has primarily relied on static benchmarks, including HumanEval (Chen et al., 2021), or more recently using human preferences of LLM res…
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor +35
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (trai…
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
Hussein Mozannar, Jimin J Lee, Dennis Wei +3
People are relying on AI agents to assist them with various tasks. The human must know when to rely on the agent, collaborate with the agent, or ignore its suggestions. In this wor…