151 citations · 297 across the 22 of their papers we have counts for
45 papers · 1 filter
Instruction-following Evaluation through Verbalizer Manipulation
Shiyang Li, Jun Yan, Hai Wang +4
While instruction-tuned models have shown remarkable success in various natural language processing tasks, accurately evaluating their ability to follow instructions remains challe…
LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion
Dongfu Jiang, Xiang Ren, Bill Yuchen Lin
We present LLM-Blender, an ensembling framework designed to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language mode…
GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions
Woojeong Jin, Subhabrata Mukherjee, Yu Cheng +5
Generalization to unseen tasks is an important ability for few-shot learners to achieve better zero-/few-shot performance on diverse tasks. However, such generalization to vision-l…
SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks
Bill Yuchen Lin, Yicheng Fu, Karina Yang +6
We introduce SwiftSage, a novel agent framework inspired by the dual-process theory of human cognition, designed to excel in action planning for complex interactive reasoning tasks…
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
Ximing Lu, Faeze Brahman, Peter West +14
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure promptin…
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-bench
Qinyuan Ye, Harvey Yiyun Fu, Xiang Ren +1
We investigate the predictability of large language model (LLM) capabilities: given records of past experiments using different model families, numbers of parameters, tasks, and nu…