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20232025
most citedLarge Language Models can Learn Rules

8 citations · 13 across the 9 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.CL2024★ 2 cited

Autoregressive Large Language Models are Computationally Universal

Dale Schuurmans, Hanjun Dai, Francesco Zanini

We show that autoregressive decoding of a transformer-based language model can realize universal computation, without external intervention or modification of the model's weights.…

cs.LG2024

Matryoshka Pilot: Learning to Drive Black-Box LLMs with LLMs

Changhao Li, Yuchen Zhuang, Rushi Qiang +4

Despite the impressive generative abilities of black-box large language models (LLMs), their inherent opacity hinders further advancements in capabilities such as reasoning, planni…

cs.LG2024

Faster WIND: Accelerating Iterative Best-of- Distillation for LLM Alignment

Tong Yang, Jincheng Mei, Hanjun Dai +5

Recent advances in aligning large language models with human preferences have corroborated the growing importance of best-of-N distillation (BOND). However, the iterative BOND algo…

cs.DB2024★ 2 cited

UQE: A Query Engine for Unstructured Databases

Hanjun Dai, Bethany Yixin Wang, Xingchen Wan +7

Analytics on structured data is a mature field with many successful methods. However, most real world data exists in unstructured form, such as images and conversations. We investi…

cs.CL2024★ 1 cited

Exploring and Benchmarking the Planning Capabilities of Large Language Models

Bernd Bohnet, Azade Nova, Aaron T Parisi +6

Classical and natural language planning tasks remain a difficult domain for modern large language models (LLMs). In this work, we lay the foundations for improving planning capabil…

cs.LG2024

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

Shicong Cen, Jincheng Mei, Katayoon Goshvadi +6

Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of pr…

cs.LG2024

Beyond Expectations: Learning with Stochastic Dominance Made Practical

Shicong Cen, Jincheng Mei, Hanjun Dai +3

Stochastic dominance serves as a general framework for modeling a broad spectrum of decision preferences under uncertainty, with risk aversion as one notable example, as it natural…