2.7k citations · 4.2k across the 18 of their papers we have counts for
20 papers
CT: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
S M Rafiuddin, Atriya Sen
Sentiment in social-media threads does not only vary across posts; it shifts as users react to claims, corrections, evidence, and hostility within a branching reply tree. We study…
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
Multi-Turn Puzzles: Evaluating Interactive Reasoning and Strategic Dialogue in LLMs
Kartikeya Badola, Jonathan Simon, Arian Hosseini +7
Large language models (LLMs) excel at solving problems with clear and complete statements, but often struggle with nuanced environments or interactive tasks which are common in mos…
Seed-Free Synthetic Data Generation Framework for Instruction-Tuning LLMs: A Case Study in Thai
Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet +1
We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We id…
Gemma 2: Improving Open Language Models at a Practical Size
Gemma Team, Morgane Riviere, Shreya Pathak +195
In this work, we introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models, ranging in scale from 2 billion to 27 billion parameters. In th…
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Gemini Team, Petko Georgiev, Ving Ian Lei +1132
In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over…
Towards Coherent and Consistent Use of Entities in Narrative Generation
Pinelopi Papalampidi, Kris Cao, Tomas Kocisky
Large pre-trained language models (LMs) have demonstrated impressive capabilities in generating long, fluent text; however, there is little to no analysis on their ability to maint…
Mind the Gap: Assessing Temporal Generalization in Neural Language Models
Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya +11
Our world is open-ended, non-stationary, and constantly evolving; thus what we talk about and how we talk about it change over time. This inherent dynamic nature of language contra…
Learning and Evaluating General Linguistic Intelligence
Dani Yogatama, Cyprien de Masson d'Autume, Jerome Connor +8
We define general linguistic intelligence as the ability to reuse previously acquired knowledge about a language's lexicon, syntax, semantics, and pragmatic conventions to adapt to…
Encoding Spatial Relations from Natural Language
Tiago Ramalho, Tomáš Kočiský, Frederic Besse +5
Natural language processing has made significant inroads into learning the semantics of words through distributional approaches, however representations learnt via these methods fa…
Pushing the bounds of dropout
Gábor Melis, Charles Blundell, Tomáš Kočiský +3
We show that dropout training is best understood as performing MAP estimation concurrently for a family of conditional models whose objectives are themselves lower bounded by the o…
The NarrativeQA Reading Comprehension Challenge
Tomáš Kočiský, Jonathan Schwarz, Phil Blunsom +4
Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full documen…
The Neural Noisy Channel
Lei Yu, Phil Blunsom, Chris Dyer +2
We formulate sequence to sequence transduction as a noisy channel decoding problem and use recurrent neural networks to parameterise the source and channel models. Unlike direct mo…
Semantic Parsing with Semi-Supervised Sequential Autoencoders
Tomáš Kočiský, Gábor Melis, Edward Grefenstette +4
We present a novel semi-supervised approach for sequence transduction and apply it to semantic parsing. The unsupervised component is based on a generative model in which latent se…
Optimizing Performance of Recurrent Neural Networks on GPUs
Jeremy Appleyard, Tomas Kocisky, Phil Blunsom
As recurrent neural networks become larger and deeper, training times for single networks are rising into weeks or even months. As such there is a significant incentive to improve…
Latent Predictor Networks for Code Generation
Wang Ling, Edward Grefenstette, Karl Moritz Hermann +4
Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates…
Reasoning about Entailment with Neural Attention
Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann +2
While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pi…
Teaching Machines to Read and Comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette +4
Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents…
Learning Bilingual Word Representations by Marginalizing Alignments
Tomáš Kočiský, Karl Moritz Hermann, Phil Blunsom
We present a probabilistic model that simultaneously learns alignments and distributed representations for bilingual data. By marginalizing over word alignments the model captures…
Aggregation and Ordering in Factorised Databases
Nurzhan Bakibayev, Tomáš Kočiský, Dan Olteanu +1
A common approach to data analysis involves understanding and manipulating succinct representations of data. In earlier work, we put forward a succinct representation system for re…