Publications

Fleurs-SLU: A Massively Multilingual Benchmark for Spoken Language Understanding

Fabian David Schmidt

Ivan Vulić

Goran Glavaš

Spoken language understanding (SLU) is indispensable for half of all living languages that lack a formal writing system, since these languag… (voir plus)es cannot pair automatic speech recognition (ASR) with language models to benefit from language technology. Even if low-resource languages possess a writing system, ASR for these languages remains unreliable due to limited bimodal speech and text training data. Better SLU can strengthen the robustness of massively multilingual ASR by levering language semantics to disambiguate utterances via context or exploiting semantic similarities across languages. However, the evaluation of multilingual SLU remains limited to shallow tasks such as intent classification or language identification. To address this, we present Fleurs-SLU, a multilingual SLU benchmark that encompasses (i) 692 hours of speech for topical utterance classification in 102 languages and (ii) multiple-choice question answering through listening comprehension spanning 944 hours of speech across 92 languages. We extensively evaluate both end-to-end speech classification models and cascaded systems that combine speech-to-text transcription with subsequent classification by large language models on Fleurs-SLU. Our results show that cascaded systems exhibit greater robustness in multilingual SLU tasks, though speech encoders can achieve competitive performance in topical speech classification when appropriately pre-trained. We further find a strong correlation between robust multilingual ASR, effective speech-to-text translation, and strong multilingual SLU, highlighting the mutual benefits between acoustic and semantic speech representations.

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

doi.org

openreview.net

Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?

Anthony GX-Chen

Rob Fergus

Kenneth Marino

Language model (LM) agents are increasingly used as autonomous decision-makers who need to actively gather information to guide their decisi… (voir plus)ons. A crucial cognitive skill for such agents is the efficient exploration and understanding of the causal structure of the world -- key to robust, scientifically grounded reasoning. Yet, it remains unclear whether LMs possess this capability or exhibit systematic biases leading to erroneous conclusions. In this work, we examine LMs' ability to explore and infer causal relationships, using the well-established"Blicket Test"paradigm from developmental psychology. We find that LMs reliably infer the common, intuitive disjunctive causal relationships but systematically struggle with the unusual, yet equally (or sometimes even more) evidenced conjunctive ones. This"disjunctive bias"persists across model families, sizes, and prompting strategies, and performance further declines as task complexity increases. Interestingly, an analogous bias appears in human adults, suggesting that LMs may have inherited deep-seated reasoning heuristics from their training data. To this end, we quantify similarities between LMs and humans, finding that LMs exhibit adult-like inference profiles (but not children-like). Finally, we propose a test-time sampling method which explicitly samples and eliminates hypotheses about causal relationships from the LM. This scalable approach significantly reduces the disjunctive bias and moves LMs closer to the goal of scientific, causally rigorous reasoning.

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

doi.org

openreview.net

Not All Data Are Unlearned Equally

Aravind Krishnan

Siva Reddy

Marius Mosbach

Machine unlearning is concerned with the task of removing knowledge learned from particular data points from a trained model. In the context… (voir plus) of large language models (LLMs), unlearning has recently received increased attention, particularly for removing knowledge about named entities from models for privacy purposes. While various approaches have been proposed to address the unlearning problem, most existing approaches treat all data points to be unlearned equally, i.e., unlearning that Montreal is a city in Canada is treated exactly the same as unlearning the phone number of the first author of this paper. In this work, we show that this all data is equal assumption does not hold for LLM unlearning. We study how the success of unlearning depends on the frequency of the knowledge we want to unlearn in the pre-training data of a model and find that frequency strongly affects unlearning, i.e., more frequent knowledge is harder to unlearn. Additionally, we uncover a misalignment between probability and generation-based evaluations of unlearning and show that this problem worsens as models become larger. Overall, our experiments highlight the need for better evaluation practices and novel methods for LLM unlearning that take the training data of models into account.

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

doi.org

openreview.net

Partial Perspectives: How LLMs Handle Logically Inconsistent Knowledge in Reasoning Tasks

Zichao Li

Ines Arous

Jackie Cheung

Most natural language reasoning tasks in the research community assume consistent input knowledge. Nevertheless, real-world scenarios often … (voir plus)involve inconsistent information, which might lead to divergent conclusions and are typically associated with varying levels of uncertainty. This raises a key research question: can large language models (LLMs) effectively handle uncertainty in their reasoning process to maximize knowledge consistency? In this paper, we propose a framework for evaluating reasoning over inconsistent knowledge. Our approach models uncertainty via weights of logical rules, leveraging Markov logic networks (MLN), which integrate probabilistic reasoning with first-order logic. This enables us to quantify inconsistencies in knowledge bases, and hence rigorously evaluate LLM reasoning. We introduce two tasks using this framework: 1) QA, which involves answering questions by integrating inconsistent knowledge; and 2) knowledge rectification, where we aim to rectify language models' acquired knowledge to improve consistency. We curate a dataset of 3,000 MLN-formatted knowledge bases to implement these tasks. We evaluate state-of-the-art LLMs on these tasks and highlight their limitations in uncertainty-aware reasoning over inconsistent logical knowledge.

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

openreview.net

Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers

Morgane M Moss

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

doi.org

openreview.net

Steering Large Language Model Activations in Sparse Spaces

Reza Bayat

Ali Rahimi-Kalahroudi

Mohammad Pezeshki

Sarath Chandar

Pascal Vincent

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

doi.org

openreview.net

Training Plug-and-Play Knowledge Modules with Deep Context Distillation

Lucas Caccia

Alan Ansell

Edoardo Ponti

Ivan Vulić

Alessandro Sordoni

Dynamically integrating new or rapidly evolving information after (Large) Language Model pre-training remains challenging, particularly in l… (voir plus)ow-data scenarios or when dealing with private and specialized documents. In-context learning and retrieval-augmented generation (RAG) face limitations, including their high inference costs and their inability to capture global document information. In this paper, we propose a way of modularizing knowledge by training document-level Knowledge Modules (KMs). KMs are lightweight components implemented as parameter-efficient LoRA modules, which are trained to store information about new documents and can be easily plugged into models on demand. We show that next-token prediction performs poorly as the training objective for KMs. We instead propose Deep Context Distillation: we learn KMs parameters such as to simulate hidden states and logits of a teacher that takes the document in context. Our method outperforms standard next-token prediction and pre-instruction training techniques, across two datasets. Finally, we highlight synergies between KMs and retrieval-augmented generation.

2025-07-07

colmweb.org/COLM/2025/Conference (accepté)

openreview.net

DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads

Kailu Song

Yumin Zheng

Bowen Zhao

David H. Eidelman

Jian Tang

Jun Ding

2025-07-04

Nature Communications (publié)

doi.org

Extracting and Following Paths for Robust Relational Reasoning with Large Language Models

Ge Zhang

Mohammad Alomrani

Hongjian Gu

Jiaming Zhou

Yaochen Hu

B. Wang

Qun Liu

Mark Coates

Yingxue Zhang

Jianye Hao

2025-07-04

KDD.org/2025/Workshop/SKnow-LLM (présentation orale)

openreview.net

Assemblies, synapse clustering, and network topology interact with plasticity to explain structure-function relationships of the cortical connectome

András Ecker

Daniela Egas Santander

Marwan Abdellah

Jorge Blanco Alonso

Sirio Bolaños-Puchet

Giuseppe Chindemi

Dhuruva Priyan Gowri Mariyappan

James B. Isbister

James King

Pramod Kumbhar

Ioannis Magkanaris

Eilif Benjamin Muller

Michael W. Reimann

Synaptic plasticity underlies the brain’s ability to learn and adapt. While experiments in brain slices have revealed mechanisms and proto… (voir plus)cols for the induction of plasticity between pairs of neurons, how these synaptic changes are coordinated in biological neuronal networks to ensure the emergence of learning remains poorly understood. Simulation and modeling have emerged as important tools to study learning in plastic networks, but have yet to achieve a scale that incorporates realistic network structure, active dendrites, and multi-synapse interactions, key determinants of synaptic plasticity. To rise to this challenge, we endowed an existing large-scale cortical network model, incorporating data-constrained dendritic processing and multi-synaptic connections, with a calcium-based model of functional plasticity that captures the diversity of excitatory connections extrapolated to in vivo-like conditions. This allowed us to study how dendrites and network structure interact with plasticity to shape stimulus representations at the microcircuit level. In our exploratory simulations, plasticity acted sparsely and specifically, firing rates and weight distributions remained stable without additional homeostatic mechanisms. At the circuit level, we found plasticity was driven by co-firing stimulus-evoked functional assemblies, spatial clustering of synapses on dendrites, and the topology of the network connectivity. As a result of the plastic changes, the network became more reliable with more stimulus-specific responses. We confirmed our testable predictions in the MICrONS datasets, an openly available electron microscopic reconstruction of a large volume of cortical tissue. Our results quantify at a large scale how the dendritic architecture and higher-order structure of cortical microcircuits play a central role in functional plasticity and provide a foundation for elucidating their role in learning.

2025-07-03

eLife (publié)

doi.org

Toward whole-genome inference of polygenic scores with fast and memory-efficient algorithms.

Shadi Zabad

Chirayu Anant Haryan

Simon Gravel

Sanchit Misra

Yue Li

2025-07-03

American Journal of Human Genetics (publié)

doi.org