8 papers
When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking
Orian Dabod, Amir DN Cohen, Gabriel Stanovsky
Few-shot selection typically assumes that reranking retrieved examples always improves performance. We challenge this view by identifying that the expensive reranking step can in f…
Comparing the Framing Effect in Humans and LLMs on Naturally Occurring Texts
Gili Lior, Liron Nacchace, Gabriel Stanovsky
Humans are influenced by how information is presented, a phenomenon known as the framing effect. Prior work suggests that LLMs may also be susceptible to framing, but it has relied…
More Documents, Same Length: Isolating the Challenge of Multiple Documents in RAG
Shahar Levy, Nir Mazor, Lihi Shalmon +2
Retrieval-Augmented Generation (RAG) enhances the accuracy of Large Language Model (LLM) responses by leveraging relevant external documents during generation. Although previous st…
Cooking Up Creativity: Enhancing LLM Creativity through Structured Recombination
Moran Mizrahi, Chen Shani, Gabriel Stanovsky +2
Large Language Models (LLMs) excel at many tasks, yet they struggle to produce truly creative, diverse ideas. In this paper, we introduce a novel approach that enhances LLM creativ…
Time to Talk: LLM Agents for Asynchronous Group Communication in Mafia Games
Niv Eckhaus, Uri Berger, Gabriel Stanovsky
LLMs are used predominantly in synchronous communication, where a human user and a model communicate in alternating turns. In contrast, many real-world settings are asynchronous. F…
Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis
Uri Berger, Gabriel Stanovsky, Omri Abend +1
The task of image captioning has recently been gaining popularity, and with it the complex task of evaluating the quality of image captioning models. In this work, we present the f…