activity
20172026
most citedNeural Text Generation with Unlikelihood Training

241 citations · 983 across the 67 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CL2023

STEER: Unified Style Transfer with Expert Reinforcement

Skyler Hallinan, Faeze Brahman, Ximing Lu +3

While text style transfer has many applications across natural language processing, the core premise of transferring from a single source style is unrealistic in a real-world setti…

cs.AI2023★ 3 cited

LLMSTEP: LLM proofstep suggestions in Lean

Sean Welleck, Rahul Saha

We present LLMSTEP, a tool for integrating a language model into the Lean proof assistant. LLMSTEP is a Lean 4 tactic that sends a user's proof state to a server hosting a language…

cs.CL2023★ 15 cited

Llemma: An Open Language Model For Mathematics

Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster +6

We present Llemma, a large language model for mathematics. We continue pretraining Code Llama on the Proof-Pile-2, a mixture of scientific papers, web data containing mathematics,…

cs.CL2023★ 71 cited

Faith and Fate: Limits of Transformers on Compositionality

Nouha Dziri, Ximing Lu, Melanie Sclar +13

Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneo…

cs.CL2023

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…

cs.CL2023★ 221 cited

Self-Refine: Iterative Refinement with Self-Feedback

Aman Madaan, Niket Tandon, Prakhar Gupta +13

Like humans, large language models (LLMs) do not always generate the best output on their first try. Motivated by how humans refine their written text, we introduce Self-Refine, an…