252 citations · 2k across the 56 of their papers we have counts for
57 papers
Response to Promises and Pitfalls of Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov +1
This note responds to "Promises and Pitfalls of Deep Kernel Learning" (Ober et al., 2021). The marginal likelihood of a Gaussian process can be compartmentalized into a data fit te…
PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization
Xinyuan Wang, Chenxi Li, Zhen Wang +6
Highly effective, task-specific prompts are often heavily engineered by experts to integrate detailed instructions and domain insights based on a deep understanding of both instinc…
Text Alignment Is An Efficient Unified Model for Massive NLP Tasks
Yuheng Zha, Yichi Yang, Ruichen Li +1
Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is of…
AlignScore: Evaluating Factual Consistency with a Unified Alignment Function
Yuheng Zha, Yichi Yang, Ruichen Li +1
Many text generation applications require the generated text to be factually consistent with input information. Automatic evaluation of factual consistency is challenging. Previous…
Reasoning with Language Model is Planning with World Model
Shibo Hao, Yi Gu, Haodi Ma +4
Large language models (LLMs) have shown remarkable reasoning capabilities, especially when prompted to generate intermediate reasoning steps (e.g., Chain-of-Thought, CoT). However,…
ToolkenGPT: Augmenting Frozen Language Models with Massive Tools via Tool Embeddings
Shibo Hao, Tianyang Liu, Zhen Wang +1
Augmenting large language models (LLMs) with external tools has emerged as a promising approach to solving complex problems. However, traditional methods, which finetune LLMs with…