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cs.CL2025
Towards Better Instruction Following Retrieval Models
Yuchen Zhuang, Aaron Trinh, Rushi Qiang +4
Modern information retrieval (IR) models, trained exclusively on standard <query, passage> pairs, struggle to effectively interpret and follow explicit user instructions. We introd…
cs.CL2024
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.CL2024
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.…