2 citations · 4 across the 14 of their papers we have counts for
5 papers · 1 filter
Alignment Makes Language Models Normative, Not Descriptive
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
Post-training alignment optimizes language models to match human preference signals, but this objective is not equivalent to modeling observed human behavior. We compare 120 base-a…
TabAgent: Distilling LLM Agent Components into Cheap Classifiers
Ido Levy, Eilam Shapira, Yinon Goldshtein +5
Agentic systems, AI architectures that autonomously execute multi-step workflows to achieve complex goals, are often built using repeated large language model (LLM) calls for close…
Textual Planning with Explicit Latent Transitions
Eliezer Shlomi, Ido Levy, Eilam Shapira +6
Planning requires a transition model that predicts how each action changes the current state. When a large language model (LLM) plays this role, every next state is generated token…
Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning
Kajetan Dymkiewicz, Ivan Vulic, Helen Yannakoudakis +3
Large language models (LLMs) perform strongly across tasks and languages, yet how improvements in one task or language affect other tasks and languages remains poorly understood. W…
GLEE: A Unified Framework and Benchmark for Language-based Economic Environments
Eilam Shapira, Omer Madmon, Itamar Reinman +3
Large Language Models (LLMs) show significant potential in economic and strategic interactions, where communication via natural language is often prevalent. This raises key questio…