85 citations · 203 across the 27 of their papers we have counts for
22 papers · 1 filter
zip2zip: Inference-Time Adaptive Tokenization via Online Compression
Saibo Geng, Nathan Ranchin, Yunzhen yao +4
Tokenization efficiency plays a critical role in the performance and cost of large language models (LLMs), yet most models rely on static tokenizers optimized on general-purpose co…
Agentic AI: The Era of Semantic Decoding
Maxime Peyrard, Martin Josifoski, Robert West
Recent work demonstrated great promise in the idea of orchestrating collaborations between LLMs, human input, and various tools to address the inherent limitations of LLMs. We prop…
Evaluating Language Model Agency through Negotiations
Tim R. Davidson, Veniamin Veselovsky, Martin Josifoski +4
We introduce an approach to evaluate language model (LM) agency using negotiation games. This approach better reflects real-world use cases and addresses some of the shortcomings o…
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia
Giovanni Monea, Maxime Peyrard, Martin Josifoski +6
Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unkno…
Fly-Swat or Cannon? Cost-Effective Language Model Choice via Meta-Modeling
Marija Šakota, Maxime Peyrard, Robert West
Generative language models (LMs) have become omnipresent across data science. For a wide variety of tasks, inputs can be phrased as natural language prompts for an LM, from whose o…
Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning
Saibo Geng, Martin Josifoski, Maxime Peyrard +1
Despite their impressive performance, large language models (LMs) still struggle with reliably generating complex output structures when not finetuned to follow the required output…
REFINER: Reasoning Feedback on Intermediate Representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard +4
Language models (LMs) have recently shown remarkable performance on reasoning tasks by explicitly generating intermediate inferences, e.g., chain-of-thought prompting. However, the…
Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction
Martin Josifoski, Marija Sakota, Maxime Peyrard +1
Large language models (LLMs) have great potential for synthetic data generation. This work shows that useful data can be synthetically generated even for tasks that cannot be solve…
Language Model Decoding as Likelihood-Utility Alignment
Martin Josifoski, Maxime Peyrard, Frano Rajic +8
A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re…
The Glass Ceiling of Automatic Evaluation in Natural Language Generation
Pierre Colombo, Maxime Peyrard, Nathan Noiry +2
Automatic evaluation metrics capable of replacing human judgments are critical to allowing fast development of new methods. Thus, numerous research efforts have focused on crafting…
Descartes: Generating Short Descriptions of Wikipedia Articles
Marija Sakota, Maxime Peyrard, Robert West
Wikipedia is one of the richest knowledge sources on the Web today. In order to facilitate navigating, searching, and maintaining its content, Wikipedia's guidelines state that all…
On the Context-Free Ambiguity of Emoji
Justyna Czestochowska, Kristina Gligoric, Maxime Peyrard +6
Emojis come with prepacked semantics making them great candidates to create new forms of more accessible communications. Yet, little is known about how much of this emojis semantic…
GenIE: Generative Information Extraction
Martin Josifoski, Nicola De Cao, Maxime Peyrard +2
Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object)…
Better than Average: Paired Evaluation of NLP Systems
Maxime Peyrard, Wei Zhao, Steffen Eger +1
Evaluation in NLP is usually done by comparing the scores of competing systems independently averaged over a common set of test instances. In this work, we question the use of aver…
Invariant Language Modeling
Maxime Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski +5
Large pretrained language models are critical components of modern NLP pipelines. Yet, they suffer from spurious correlations, poor out-of-domain generalization, and biases. Inspir…
Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?
Maxime Peyrard, Beatriz Borges, Kristina Gligorić +1
The automatic detection of humor poses a grand challenge for natural language processing. Transformer-based systems have recently achieved remarkable results on this task, but they…
KLearn: Background Knowledge Inference from Summarization Data
Maxime Peyrard, Robert West
The goal of text summarization is to compress documents to the relevant information while excluding background information already known to the receiver. So far, summarization rese…
On the Limitations of Cross-lingual Encoders as Exposed by Reference-Free Machine Translation Evaluation
Wei Zhao, Goran Glavaš, Maxime Peyrard +3
Evaluation of cross-lingual encoders is usually performed either via zero-shot cross-lingual transfer in supervised downstream tasks or via unsupervised cross-lingual textual simil…
MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
Wei Zhao, Maxime Peyrard, Fei Liu +3
A robust evaluation metric has a profound impact on the development of text generation systems. A desirable metric compares system output against references based on their semantic…
Concatenated Power Mean Word Embeddings as Universal Cross-Lingual Sentence Representations
Andreas Rücklé, Steffen Eger, Maxime Peyrard +1
Average word embeddings are a common baseline for more sophisticated sentence embedding techniques. However, they typically fall short of the performances of more complex models su…
Live Blog Corpus for Summarization
Avinesh P. V. S., Maxime Peyrard, Christian M. Meyer
Live blogs are an increasingly popular news format to cover breaking news and live events in online journalism. Online news websites around the world are using this medium to give…
A Simple Theoretical Model of Importance for Summarization
Maxime Peyrard
Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notion of information Importance remaining…