most citedCoheSentia: A Novel Benchmark of Incremental versus Holistic Assessment of Coherence in Generated Texts

1 citations · 2 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CL2024

Mitigating Hallucinations in Large Vision-Language Models (LVLMs) via Language-Contrastive Decoding (LCD)

Avshalom Manevich, Reut Tsarfaty

Large Vision-Language Models (LVLMs) are an extension of Large Language Models (LLMs) that facilitate processing both image and text inputs, expanding AI capabilities. However, LVL…

cs.CL2024

NoviCode: Generating Programs from Natural Language Utterances by Novices

Asaf Achi Mordechai, Yoav Goldberg, Reut Tsarfaty

Current Text-to-Code models demonstrate impressive capabilities in generating executable code from natural language snippets. However, current studies focus on technical instructio…

cs.CL2024

Into the Unknown: Generating Geospatial Descriptions for New Environments

Tzuf Paz-Argaman, John Palowitch, Sayali Kulkarni +2

Similar to vision-and-language navigation (VLN) tasks that focus on bridging the gap between vision and language for embodied navigation, the new Rendezvous (RVS) task requires rea…

cs.CL2024

MRL Parsing Without Tears: The Case of Hebrew

Shaltiel Shmidman, Avi Shmidman, Moshe Koppel +1

Syntactic parsing remains a critical tool for relation extraction and information extraction, especially in resource-scarce languages where LLMs are lacking. Yet in morphologically…

cs.CL20241 cited

Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications?

Yotam Intrator, Matan Halfon, Roman Goldenberg +5

Large language models hold significant promise in multilingual applications. However, inherent biases stemming from predominantly English-centric pre-training have led to the wides…

cs.CL2024

A Truly Joint Neural Architecture for Segmentation and Parsing

Danit Yshaayahu Levi, Reut Tsarfaty

Contemporary multilingual dependency parsers can parse a diverse set of languages, but for Morphologically Rich Languages (MRLs), performance is attested to be lower than other lan…