Author name cluster

Boaz Carmeli

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

ICLR Conference 2025 Conference Paper

CtD: Composition through Decomposition in Emergent Communication

Boaz Carmeli
Ron Meir
Yonatan Belinkov

Compositionality is a cognitive mechanism that allows humans to systematically combine known concepts in novel ways. This study demonstrates how artificial neural agents acquire and utilize compositional generalization to describe previously unseen images. Our method, termed \`\`Composition through Decomposition'', involves two sequential training steps. In the \'Decompose\' step, the agents learn to decompose an image into basic concepts using a codebook acquired during interaction in a multi-target coordination game. Subsequently, in the \`Compose\' step, the agents employ this codebook to describe novel images by composing basic concepts into complex phrases. Remarkably, we observe cases where generalization in the `Compose' step is achieved zero-shot, without the need for additional training.

Details

AAAI Conference 2025 Conference Paper

Unsupervised Translation of Emergent Communication

Ido Levy
Orr Paradise
Boaz Carmeli
Ron Meir
Shafi Goldwasser
Yonatan Belinkov

Emergent Communication (EC) provides a unique window into the language systems that emerge autonomously when agents are trained to jointly achieve shared goals. However, it is difficult to interpret EC and evaluate its relationship with natural languages (NL). This study employs unsupervised neural machine translation (UNMT) techniques to decipher ECs formed during referential games with varying task complexities, influenced by the semantic diversity of the environment. Our findings demonstrate UNMT's potential to translate EC, illustrating that task complexity characterized by semantic diversity enhances EC translatability, while higher task complexity with constrained semantic variability exhibits pragmatic EC, which, although challenging to interpret, remains suitable for translation. This research marks the first attempt, to our knowledge, to translate EC without the aid of parallel data.

PDF Details DOI

ICLR Conference 2024 Conference Paper

Achieving Human Parity in Content-Grounded Datasets Generation

Asaf Yehudai
Boaz Carmeli
Yosi Mass
Ofir Arviv
Nathaniel Mills
Eyal Shnarch
Leshem Choshen

The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel method for automatically generating high-quality content-grounded data. It consists of three stages: (a) Content Preparation, (b) Generation: creating task-specific examples from the content (e.g., question-answer pairs or summaries). (c) Filtering mechanism aiming to ensure the quality and faithfulness of the generated data. We showcase this methodology by generating three large-scale synthetic data, making wishes, for Long-Form Question-Answering (LFQA), summarization, and information extraction. In a human evaluation, our generated data was found to be natural and of high quality. Furthermore, we compare models trained on our data with models trained on human-written data -- ELI5 and ASQA for LFQA and CNN-DailyMail for Summarization. We show that our models are on par with or outperforming models trained on human-generated data and consistently outperforming them in faithfulness. Finally, we applied our method to create LFQA data within the medical domain and compared a model trained on it with models trained on other domains.

Details

NeurIPS Conference 2024 Conference Paper

Semantics and Spatiality of Emergent Communication

Rotem Ben Zion
Boaz Carmeli
Orr Paradise
Yonatan Belinkov

When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols. Good task performance is often considered sufficient evidence that meaningful communication is taking place, but existing empirical results show that communication strategies induced by common objectives can be counterintuitive whilst solving the task nearly perfectly. In this work, we identify a goal-agnostic prerequisite to meaningful communication, which we term semantic consistency, based on the idea that messages should have similar meanings across instances. We provide a formal definition for this idea, and use it to compare the two most common objectives in the field of emergent communication: discrimination and reconstruction. We prove, under mild assumptions, that semantically inconsistent communication protocols can be optimal solutions to the discrimination task, but not to reconstruction. We further show that the reconstruction objective encourages a stricter property, spatial meaningfulness, which also accounts for the distance between messages. Experiments with emergent communication games validate our theoretical results. These findings demonstrate an inherent advantage of distance-based communication goals, and contextualize previous empirical discoveries.

PDF Details DOI

AAAI Conference 2023 Conference Paper

Emergent Quantized Communication

Boaz Carmeli
Ron Meir
Yonatan Belinkov

The field of emergent communication aims to understand the characteristics of communication as it emerges from artificial agents solving tasks that require information exchange. Communication with discrete messages is considered a desired characteristic, for scientific and applied reasons. However, training a multi-agent system with discrete communication is not straightforward, requiring either reinforcement learning algorithms or relaxing the discreteness requirement via a continuous approximation such as the Gumbel-softmax. Both these solutions result in poor performance compared to fully continuous communication. In this work, we propose an alternative approach to achieve discrete communication -- quantization of communicated message. Using message quantization allows us to train the model end-to-end, achieving superior performance in multiple setups. Moreover, quantization is a natural framework that runs the gamut from continuous to discrete communication. Thus, it sets the ground for a broader view of multi-agent communication in the deep learning era.

PDF Details DOI

ICLR Conference 2023 Conference Paper

QAID: Question Answering Inspired Few-shot Intent Detection

Asaf Yehudai
Matan Vetzler
Yosi Mass
Koren Lazar
Doron Cohen 0001
Boaz Carmeli

Intent detection with semantically similar fine-grained intents is a challenging task. To address it, we reformulate intent detection as a question-answering retrieval task by treating utterances and intent names as questions and answers. To that end, we utilize a question-answering retrieval architecture and adopt a two stages training schema with batch contrastive loss. In the pre-training stage, we improve query representations through self-supervised training. Then, in the fine-tuning stage, we increase contextualized token-level similarity scores between queries and answers from the same intent. Our results on three few-shot intent detection benchmarks achieve state-of-the-art performance.

Details

AAAI Conference 2020 Conference Paper

Do Not Have Enough Data? Deep Learning to the Rescue!

Ateret Anaby-Tavor
Boaz Carmeli
Esther Goldbraich
Amir Kantor
George Kour
Segev Shlomov
Naama Tepper
Naama Zwerdling

Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classiﬁcation tasks. We use a powerful pre-trained neural network model to artiﬁcially synthesize new labeled data for supervised learning. We mainly focus on cases with scarce labeled data. Our method, referred to as language-model-based data augmentation (LAM- BADA), involves ﬁne-tuning a state-of-the-art language generator to a speciﬁc task through an initial training phase on the existing (usually small) labeled data. Using the ﬁne-tuned model and given a class label, new sentences for the class are generated. Our process then ﬁlters these new sentences by using a classiﬁer trained on the original data. In a series of experiments, we show that LAMBADA improves classi- ﬁers’ performance on a variety of datasets. Moreover, LAM- BADA signiﬁcantly improves upon the state-of-the-art techniques for data augmentation, speciﬁcally those applicable to text classiﬁcation tasks with little data.

PDF Details