52 citations · 224 across the 34 of their papers we have counts for
38 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Robostral Navigate
Abdelaziz Bounhar, Abhijeet Somani, Aditi Kabra +273
Deploying navigation systems at scale requires a recipe that minimizes sensor assumptions, generalizes across robot embodiments, and trains efficiently. Yet, today's best systems d…
Voxtral TTS
Mistral-AI, :, Alexander H. Liu +186
We introduce Voxtral TTS, an expressive multilingual text-to-speech model that generates natural speech from as little as 3 seconds of reference audio. Voxtral TTS adopts a hybrid…
Voxtral
Alexander H. Liu, Andy Ehrenberg, Andy Lo +103
We present Voxtral Mini and Voxtral Small, two multimodal audio chat models. Voxtral is trained to comprehend both spoken audio and text documents, achieving state-of-the-art perfo…
Magistral
Mistral-AI, :, Abhinav Rastogi +98
We introduce Magistral, Mistral's first reasoning model and our own scalable reinforcement learning (RL) pipeline. Instead of relying on existing implementations and RL traces dist…
Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness
Khyathi Raghavi Chandu, Linjie Li, Anas Awadalla +5
The ability to acknowledge the inevitable uncertainty in their knowledge and reasoning is a prerequisite for AI systems to be truly truthful and reliable. In this paper, we present…
On the Role of Summary Content Units in Text Summarization Evaluation
Marcel Nawrath, Agnieszka Nowak, Tristan Ratz +13
At the heart of the Pyramid evaluation method for text summarization lie human written summary content units (SCUs). These SCUs are concise sentences that decompose a summary into…
RewardBench: Evaluating Reward Models for Language Modeling
Nathan Lambert, Valentina Pyatkin, Jacob Morrison +9
Reward models (RMs) are at the crux of successfully using RLHF to align pretrained models to human preferences, yet there has been relatively little study that focuses on evaluatio…
L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects
Yutaro Yamada, Khyathi Chandu, Yuchen Lin +3
Diffusion-based image generation models such as DALL-E 3 and Stable Diffusion-XL demonstrate remarkable capabilities in generating images with realistic and unique compositions. Ye…
Deal, or no deal (or who knows)? Forecasting Uncertainty in Conversations using Large Language Models
Anthony Sicilia, Hyunwoo Kim, Khyathi Raghavi Chandu +2
Effective interlocutors account for the uncertain goals, beliefs, and emotions of others. But even the best human conversationalist cannot perfectly anticipate the trajectory of a…
Selective "Selective Prediction": Reducing Unnecessary Abstention in Vision-Language Reasoning
Tejas Srinivasan, Jack Hessel, Tanmay Gupta +4
Selective prediction minimizes incorrect predictions from vision-language models (VLMs) by allowing them to abstain from answering when uncertain. However, when deploying a vision-…
OLMo: Accelerating the Science of Language Models
Dirk Groeneveld, Iz Beltagy, Pete Walsh +40
Language models (LMs) have become ubiquitous in both NLP research and in commercial product offerings. As their commercial importance has surged, the most powerful models have beco…
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
Luca Soldaini, Rodney Kinney, Akshita Bhagia +33
Information about pretraining corpora used to train the current best-performing language models is seldom discussed: commercial models rarely detail their data, and even open model…
Localized Symbolic Knowledge Distillation for Visual Commonsense Models
Jae Sung Park, Jack Hessel, Khyathi Raghavi Chandu +8
Instruction following vision-language (VL) models offer a flexible interface that supports a broad range of multimodal tasks in a zero-shot fashion. However, interfaces that operat…
NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation
Peter West, Ronan Le Bras, Taylor Sorensen +8
We present NovaCOMET, an open commonsense knowledge model, that combines the best aspects of knowledge and general task models. Compared to previous knowledge models, NovaCOMET all…
The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
Bill Yuchen Lin, Abhilasha Ravichander, Ximing Lu +5
The alignment tuning process of large language models (LLMs) typically involves instruction learning through supervised fine-tuning (SFT) and preference tuning via reinforcement le…
Agent Lumos: Unified and Modular Training for Open-Source Language Agents
Da Yin, Faeze Brahman, Abhilasha Ravichander +4
Closed-source agents suffer from several issues such as a lack of affordability, transparency, and reproducibility, particularly on complex interactive tasks. This motivates the de…
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Peter West, Ximing Lu, Nouha Dziri +11
The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models…
How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources
Yizhong Wang, Hamish Ivison, Pradeep Dasigi +8
In this work we explore recent advances in instruction-tuning language models on a range of open instruction-following datasets. Despite recent claims that open models can be on pa…
Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning
Ximing Lu, Faeze Brahman, Peter West +14
While extreme-scale language models have demonstrated exceptional performance on a variety of language tasks, the degree of control over these language models through pure promptin…
Continual Dialogue State Tracking via Example-Guided Question Answering
Hyundong Cho, Andrea Madotto, Zhaojiang Lin +5
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on prev…
Curriculum Script Distillation for Multilingual Visual Question Answering
Khyathi Raghavi Chandu, Alborz Geramifard
Pre-trained models with dual and cross encoders have shown remarkable success in propelling the landscape of several tasks in vision and language in Visual Question Answering (VQA)…
Needle in a Haystack: An Analysis of High-Agreement Workers on MTurk for Summarization
Lining Zhang, Simon Mille, Yufang Hou +8
To prevent the costly and inefficient use of resources on low-quality annotations, we want a method for creating a pool of dependable annotators who can effectively complete diffic…
Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities
Khyathi Raghavi Chandu, Alborz Geramifard
Contextualizing language technologies beyond a single language kindled embracing multiple modalities and languages. Individually, each of these directions undoubtedly proliferated…