4 citations · 5 across the 4 of their papers we have counts for
4 papers · 1 filter
FlowBot: Inducing LLM Workflows with Bilevel Optimization and Textual Gradients
Hongyeon Yu, Young-Bum Kim, Yoon Kim
LLM workflows, which coordinate structured calls to individual LLMs/agents to achieve a particular goal, offer a promising path towards building powerful AI systems that can tackle…
Structured Code Representations Enable Data-Efficient Adaptation of Code Language Models
Mayank Agarwal, Yikang Shen, Bailin Wang +2
Current language models tailored for code tasks often adopt the pre-training-then-fine-tuning paradigm from natural language processing, modeling source code as plain text. This ap…
Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations
Zilu Tang, Mayank Agarwal, Alex Shypula +4
This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanat…
Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement
Linlu Qiu, Liwei Jiang, Ximing Lu +8
The ability to derive underlying principles from a handful of observations and then generalize to novel situations -- known as inductive reasoning -- is central to human intelligen…