Amal Zouaq

A. Chandar

2026-01-10

ArXiv (prépublication)

Combining Domain and Alignment Vectors Provides Better Knowledge-Safety Trade-offs in LLMs

Megh Thakkar

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Payel Das

A. Chandar

2024-12-31

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (publié)

Combining Domain and Alignment Vectors to Achieve Better Knowledge-Safety Trade-offs in LLMs

Megh Thakkar

Yash More

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Payel Das

A. Chandar

Chandar Research Lab

Mila - Québec

U. Montŕeal

AI Institute

Ibm Research

Polytechnique Montréal

There is a growing interest in training domain-expert LLMs that excel in specific technical fields compared to their general-purpose instruc… (voir plus)tion-tuned counterparts. However, these expert models often experience a loss in their safety abilities in the process, making them capable of generating harmful content. As a solution, we introduce an efficient and effective merging-based alignment method called \textsc{MergeAlign} that interpolates the domain and alignment vectors, creating safer domain-specific models while preserving their utility. We apply \textsc{MergeAlign} on Llama3 variants that are experts in medicine and finance, obtaining substantial alignment improvements with minimal to no degradation on domain-specific benchmarks. We study the impact of model merging through model similarity metrics and contributions of individual models being merged. We hope our findings open new research avenues and inspire more efficient development of safe expert LLMs.

2024-10-09

NeurIPS.cc/2024/Workshop/AFM (poster)

openreview.net

A Deep Dive into the Trade-Offs of Parameter-Efficient Preference Alignment Techniques

Megh Thakkar

Quentin Fournier

Matthew D Riemer

Pin-Yu Chen

Payel Das

A. Chandar

2024-06-06

ArXiv (prépublication)

MVP: Minimal Viable Phrase for Long Text Understanding.

A. Chandar

2023-12-31

International Conference on Language Resources and Evaluation (publié)

dblp.uni-trier.de

Assessing the Generalization Capabilities of Neural Machine Translation Models for SPARQL Query Generation

Samuel Reyd

2023-10-26

The Semantic Web – ISWC 2023 (publié)

SORBET: A Siamese Network for Ontology Embeddings Using a Distance-Based Regression Loss and BERT

Francis Gosselin

2023-10-26

The Semantic Web – ISWC 2023 (publié)

SORBETmatcher results for OAEI 2023.

Francis Gosselin

2022-12-31

OM@ISWC (publié)

dblp.uni-trier.de

Local Structure Matters Most: Perturbation Study in NLU

Prasanna Parthasarathi

Sarath Chandar

Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models … (voir plus)are surprisingly insensitive to the order of words. In this paper, we investigate this phenomenon by developing order-altering perturbations on the order of words, subwords, and characters to analyze their effect on neural models' performance on language understanding tasks. We experiment with measuring the impact of perturbations to the local neighborhood of characters and global position of characters in the perturbed texts and observe that perturbation functions found in prior literature only affect the global ordering while the local ordering remains relatively unperturbed. We empirically show that neural models, invariant of their inductive biases, pretraining scheme, or the choice of tokenization, mostly rely on the local structure of text to build understanding and make limited use of the global structure.

2022-04-30

Findings of the Association for Computational Linguistics: ACL 2022 (publié)

Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes

Prasanna Parthasarathi

Sarath Chandar

Providing better language tools for low-resource and endangered languages is imperative for equitable growth. Recent progress with massively… (voir plus) multilingual pretrained models has proven surprisingly effective at performing zero-shot transfer to a wide variety of languages. However, this transfer is not universal, with many languages not currently understood by multilingual approaches. It is estimated that only 72 languages possess a "small set of labeled datasets" on which we could test a model's performance, the vast majority of languages not having the resources available to simply evaluate performances on. In this work, we attempt to clarify which languages do and do not currently benefit from such transfer. To that end, we develop a general approach that requires only unlabelled text to detect which languages are not well understood by a cross-lingual model. Our approach is derived from the hypothesis that if a model's understanding is insensitive to perturbations to text in a language, it is likely to have a limited understanding of that language. We construct a cross-lingual sentence similarity task to evaluate our approach empirically on 350, primarily low-resource, languages.

2021-12-31

EMNLP (Findings) (publié)

Local Structure Matters Most in Most Languages

Prasanna Parthasarathi

A. Chandar

2021-12-31

AACL/IJCNLP (2) (publié)