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Approaches to improving multilingual language understanding often struggle with significant performance gaps between high-resource and low-resource languages. While there are efforts to align the languages in a single latent space to mitigate such gaps, how different input-level representations influence such gaps has not been investigated, particularly with phonemic inputs. We hypothesize that the performance gaps are affected by representation discrepancies between these languages, and revisit the use of phonemic representations as a means to mitigate these discrepancies. To demonstrate the effectiveness of phonemic representations, we present experiments on three representative cross-lingual tasks on 12 languages in total. The results show that phonemic representations exhibit higher similarities between languages compared to orthographic representations, and it consistently outperforms grapheme-based baseline model on languages that are relatively low-resourced. We present quantitative evidence from three cross-lingual tasks that demonstrate the effectiveness of phonemic representations, and it is further justified by a theoretical analysis of the cross-lingual performance gap.

EMNLP 2024

Mitigating the Linguistic Gap with Phonemic Representations for Robust Cross-lingual Transfer

phonemic representation

multilingual language modeling

cross-lingual transfer

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

Building robust and reliable machine learning systems requires models with the capacity to generalize their knowledge to interpret unseen inputs with different characteristics. Traditional language model evaluation tasks lack informative metrics about model generalization, and their applicability in new settings is often measured using task and language-specific downstream performance, which is lacking in many languages and tasks. To address this gap, we explore a set of efficient and reliable measures that could aid in computing more information related to the generalization capability of language models, particularly in cross-lingual zero-shot settings. Our central hypothesis is that the sharpness of a model's loss landscape, i.e., the representation of loss values over its weight space, can indicate its generalization potential, with a flatter landscape suggesting better generalization. We propose a novel and stable algorithm to reliably compute the sharpness of a model optimum, and demonstrate its correlation with successful cross-lingual transfer.

Generalization Measures for Zero-Shot Cross-Lingual Transfer

Most languages could be ambiguous, which means the same conveyed text or speech, results in different actions by different readers or listeners. In this project, we propose a method to detect the ambiguity of a sentence using translation by multilingual LLMs. In particular, we hypothesize that a good machine translator should preserve the ambiguity of sentences in all target languages. Therefore, we investigate whether ambiguity is encoded in the hidden representation of a translation model or, instead, if only a single meaning is encoded. In our experiments, we have been able to predict ambiguity of sentences with high accuracy using machine translation without direct use of semantics and only based on the reconstruction error of a function that maps the forward and backward translation hidden representations to each other. The potential applications of the proposed approach span i) detecting ambiguous sentences, ii) fine-tuning existing multilingual LLMs to preserve ambiguous information, and iii) developing AI systems that can generate ambiguity-free languages when needed.

Detecting and Translating Language Ambiguity with Multilingual LLMs

We present our systems for the three tasks and five languages included in the MRL 2024 Shared Task on Multilingual Multi-task Information Retrieval: (1) Named Entity Recognition, (2) Free-form Question Answering, and (3) Multiple-choice Question Answering. For each task, we explored the impact of selecting different multilingual language models for fine-tuning across various target languages, and implemented an ensemble system that generates final outputs based on predictions from multiple fine-tuned models. All models are large language models fine-tuned on task-specific data. Our experimental results show that a more balanced dataset would yield better results. However, when training data for certain languages are scarce, fine-tuning on a large amount of English data supplemented by a small amount of ``triggering data'' in the target language can produce decent results.\footnote{Our codes will be made available at this \href{https://github.com/Senyu-Li1/MRL-2024-Shared-Task-McGill-NLP-Group}{link}.

McGill NLP Group Submission to the MRL 2024 Shared Task: Ensembling Enhances Effectiveness of Multilingual Small LMs

We present the joint CUNI and LMU submission to the MRL~2024 Shared Task on Multi-lingual Multi-task Information Retrieval. The shared task objective was to explore how we can deploy modern methods in NLP in multi-lingual low-resource settings, tested on two sub-tasks: Named-entity recognition and question answering. Our solutions to the subtasks are based on data acquisition and model adaptation. We compare the performance of our submitted systems with the translate-test approach which proved to be the most useful in the previous edition of the shared task. Our results show that using more data as well as fine-tuning recent multilingual pre-trained models leads to considerable improvements over the translate-test baseline. Our code is available at https://github.com/ufal/mrl2024-multilingual-ir-shared-task.

CUNI and LMU Submission to the MRL 2024 Shared Task on Multi-lingual Multi-task Information Retrieval

The goal of TokenSHAP is to produce a "Feature Importance" measure for prompts. This will help understand which parts of the prompt are most influential in shaping the response of a large language model (LLM)

TokenSHAP: Interpreting Large Language Models with Monte Carlo Shapley Value Estimation

Since the advent of CRISPR-Cas9, a ground- breaking gene-editing technology that enables precise genomic modifications via a short RNA guide sequence, there has been a marked in- crease in the accessibility and application of this technology across various fields. The success of CRISPR-Cas9 has spurred further investment and led to the discovery of addi- tional CRISPR systems, including CRISPR- Cas13. Distinct from Cas9, which targets DNA, Cas13 targets RNA, offering unique ad- vantages for gene modulation. We focus on Cas13d, a variant known for its collateral activ- ity where it non-specifically cleaves adjacent RNA molecules upon activation, a feature crit- ical to its function. We introduce DeepFM- Crispr, a novel deep learning model developed to predict the on-target efficiency and evaluate the off-target effects of Cas13d. This model harnesses a large language model to generate comprehensive representations rich in evolu- tionary and structural data, thereby enhancing predictions of RNA secondary structures and overall sgRNA efficacy. A transformer-based architecture processes these inputs to produce a predictive efficacy score. Comparative exper- iments show that DeepFM-Crispr not only sur- passes traditional models but also outperforms recent state-of-the-art deep learning methods in terms of prediction accuracy and reliability.

Prediction of CRISPR On-Target Effects via Deep Learning

This paper investigates biases of Large Language Models (LLMs) through the lens of grammatical gender. Drawing inspiration from seminal works in psycholinguistics, particularly the study of gender's influence on language perception, we leverage multilingual LLMs to revisit and expand upon the foundational experiments of Boroditsky (2003). Employing LLMs as a novel method for examining psycholinguistic biases related to grammatical gender, we prompt a model to describe nouns with adjectives in various languages, focusing specifically on languages with grammatical gender. In particular, we look at adjective co-occurrences across gender and languages, and train a binary classifier to predict grammatical gender given adjectives an LLM uses to describe a noun. Surprisingly, we find that a simple classifier can not only predict noun gender above chance but also exhibit cross-language transferability. We show that while LLMs may describe words differently in different languages, they are biased similarly.

What an Elegant Bridge: Multilingual LLMs are Biased Similarly in Different Languages

This paper explores the intersection of psychology and artificial intelligence through the development and evaluation of specialized Large Language Models (LLMs). We introduce PsychoLex , a suite of resources designed to enhance LLMs' proficiency in psycho-logical tasks in both Persian and English. Key contributions include the PsychoLexQA dataset for instructional content and the PsychoLexEval dataset for rigorous evaluation of LLMs in complex psychological scenarios. Additionally, we present the PsychoLexLLaMA model, optimized specifically for psychological applications, demonstrating superior performance compared to general-purpose models. The findings underscore the potential of tailored LLMs for advancing psychological research and applications, while also highlighting areas for further refinement. This research offers a foundational step towards integrating LLMs into specialized psychological domains, with implications for future advancements in AI-driven psychological practice.

PsychoLex: Unveiling the Psychological Mind of Large Language Models

Automatic text summarization helps to digest the vast and ever-growing amount of scientific publications. While transformer-based solutions like BERT and SciBERT have advanced scientific summarization, lengthy documents pose a challenge due to the token limits of these models. To address this issue, we introduce and evaluate a two-stage model that combines an extract-then-compress framework. Our model incorporates a “graph-augmented extraction module” to select order-based salient sentences and an “abstractive compression module” to generate concise summaries. Additionally, we introduce the BioConSumm dataset, which focuses on biodiversity conservation, to support underrepresented domains and explore domain-specific summarization strategies. Out of the tested models, our model achieves the highest ROUGE-2 and ROUGE-L scores on our newly created dataset (BioConSumm) and on the SUMPUBMED dataset, which serves as a benchmark in the field of biomedicine.

Two-Stage Graph-Augmented Summarization for Scientific Documents

Instruction finetuning is a popular paradigm to align large language models (LLM) with human intent. Despite its popularity, this idea is less explored in improving LLMs to align existing foundation models with scien- tific disciplines, concepts and goals. In this work, we present SciTune as a tuning frame- work to improve the ability of LLMs to follow multimodal instructions generated from scien- tific publications. To test our methodology, we train a large multimodal model LLaMA- SciTune that connects a vision encoder and LLM for science-focused visual and language understanding. LLaMA-SciTune significantly outperforms the state-of-the-art models in the generated figure types and captions in SciCap and VisText benchmarks. In comparison to the models that are finetuned with synthetic data only, LLaMA-SciTune surpasses human performance on average and in many sub- categories on the ScienceQA benchmark. Our results demonstrate that human-generated sci- entific multimodal instructions remain highly valuable in tuning LLMs to perform well on science tasks, despite their lower volume and relative scarcity compared to synthetic data. We publicly release the SciTune codebase.

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