2 citations · 4 across the 14 of their papers we have counts for
15 papers
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In…
STRABLE: Benchmarking Tabular Machine Learning with Strings
Gioia Blayer, Myung Jun Kim, Félix Lefebvre +8
Benchmarking tabular learning has revealed the benefit of dedicated architectures, pushing the state of the art. But real-world tables often contain string entries, beyond numbers,…
MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image
Alan Arazi, Eilam Shapira, Shoham Grunblat +8
Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numeric…
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…
Sequential LLM Release Facilitates Manipulation in Regulated Markets
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents increasingly mediate bargaining, negotiation and persuasion for people and firms. Such markets extend software-mediated commerce, but add a governance problem: independen…
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…
From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production
Segev Shlomov, Alon Oved, Sami Marreed +9
Agents are rapidly advancing in automating digital work, but enterprises face a harder challenge: moving beyond prototypes to deployed systems that deliver measurable business valu…
TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields
Alan Arazi, Eilam Shapira, Roi Reichart
While deep learning has achieved remarkable success across many domains, it has historically underperformed on tabular learning tasks, which remain dominated by gradient boosting d…
Fairness under Competition
Ronen Gradwohl, Eilam Shapira, Moshe Tennenholtz
Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal…
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…
Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games
Eilam Shapira, Omer Madmon, Roi Reichart +1
Human choice prediction in economic contexts is crucial for applications in marketing, finance, public policy, and more. This task, however, is often constrained by the difficultie…
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation
Eilam Shapira, Omer Madmon, Reut Apel +2
Recent advances in Large Language Models (LLMs) have spurred interest in designing LLM-based agents for tasks that involve interaction with human and artificial agents. This paper…
Text2Model: Text-based Model Induction for Zero-shot Image Classification
Ohad Amosy, Tomer Volk, Eilam Shapira +3
We address the challenge of building task-agnostic classifiers using only text descriptions, demonstrating a unified approach to image classification, 3D point cloud classification…