12 citations · 17 across the 6 of their papers we have counts for
6 papers
s1: Simple test-time scaling
Niklas Muennighoff, Zitong Yang, Weijia Shi +7
Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability but…
Eliciting Language Model Behaviors with Investigator Agents
Xiang Lisa Li, Neil Chowdhury, Daniel D. Johnson +4
Language models exhibit complex, diverse behaviors when prompted with free-form text, making it difficult to characterize the space of possible outputs. We study the problem of beh…
Auditing Prompt Caching in Language Model APIs
Chenchen Gu, Xiang Lisa Li, Rohith Kuditipudi +2
Prompt caching in large language models (LLMs) results in data-dependent timing variations: cached prompts are processed faster than non-cached prompts. These timing differences in…
AutoBencher: Towards Declarative Benchmark Construction
Xiang Lisa Li, Farzaan Kaiyom, Evan Zheran Liu +3
We present AutoBencher, a declarative framework for automatic benchmark construction, and use it to scalably discover novel insights and vulnerabilities of existing language models…
On the Learnability of Watermarks for Language Models
Chenchen Gu, Xiang Lisa Li, Percy Liang +1
Watermarking of language model outputs enables statistical detection of model-generated text, which can mitigate harms and misuses of language models. Existing watermarking strateg…
Benchmarking and Improving Generator-Validator Consistency of Language Models
Xiang Lisa Li, Vaishnavi Shrivastava, Siyan Li +2
As of September 2023, ChatGPT correctly answers "what is 7+8" with 15, but when asked "7+8=15, True or False" it responds with "False". This inconsistency between generating and va…