Multichannel Generative Language Model: Learning All Possible Factorizations Within and Across Channels

Challenge:	MGLM is a generative joint distribution model over channels.
Approach:	They propose a multichannel generative joint distribution model over channels that marginalizes over all possible factorizations within and across all channels.
Outcome:	The proposed model outperforms traditional bilingual discriminative models.

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Approach

Outcome

Towards Unified Multimodal Large Language Models: A survey (2026.findings-acl)

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Xu Ma, Yitian Zhang, Yun Fu

Semantic Text Processing

Challenge:	unified multimodal large language models (MLLMs) are emerging but lack a systematic framework to connect them and situate current trends within a broader landscape.
Approach:	They present a systematic review of unified Multimodal Large Language Models . they outline the foundational concepts and prerequisites for understanding them .
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Cross-Lingual Generalization and Compression: From Language-Specific to Shared Neurons (2025.acl-long)

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Frederick Riemenschneider, Anette Frank

Cross-Lingual Transfer

Challenge:	Existing evidence suggests that multilingual language models can transfer knowledge across languages without explicit cross-lingual supervision.
Approach:	They analyze the parameter spaces of three multilingual language models to examine their representations . they find that models evolve from language-specific representations to more specialized layer functions .
Outcome:	The proposed model can generate coherent English text, rather than spanish text, and it can generate generalized representations, the authors show.

Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review (2023.acl-long)

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Fred Philippy, Siwen Guo, Shohreh Haddadan

Language Models

Challenge:	Pre-trained Multilingual Language Models have shown a strong ability to transfer knowledge across languages.
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Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review (2025.findings-acl)

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Pei Fu, Tongkun Guan, Zining Wang, Zhentao Guo, Chen Duan, Hao Sun, Boming Chen, Qianyi Jiang, Jiayao Ma, Kai Zhou, Junfeng Luo

Multimodality

Challenge:	Recent advances in vision-language models have unified perception and understanding tasks within Visual Question Answering paradigms.
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MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)

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Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram

Multilinguality

Challenge:	Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation.
Approach:	They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field.
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CDLM: Cross-Document Language Modeling (2021.findings-emnlp)

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Avi Caciularu, Arman Cohan, Iz Beltagy, Matthew Peters, Arie Cattan, Ido Dagan

Language Models

Challenge:	Existing language models (LMs) provide powerful representations for internal text structure, but there are important applications for multi-text tasks.
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Explainability and Interpretability of Multilingual Large Language Models: A Survey (2025.emnlp-main)

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Lucas Resck, Isabelle Augenstein, Anna Korhonen

Explainability & Interpretability in NLP

Challenge:	Existing literature on multilingual large language models lacks transparency in their internal processes.
Approach:	They propose to use multilingual large language models to examine their explainability and interpretability methods.
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Pruning Multilingual Large Language Models for Multilingual Inference (2024.findings-emnlp)

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Hwichan Kim, Jun Suzuki, Tosho Hirasawa, Mamoru Komachi

Language Models

Challenge:	Multilingual large language models (MLLMs) demonstrate better zeroshot learning performance in non-English languages compared to large language model trained on English-dominant data.
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Outcome:	The proposed pruning strategy enhances the MLLMs’ performance in non-English language.

Generative Spoken Dialogue Language Modeling (2023.tacl-1)

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Tu Anh Nguyen, Eugene Kharitonov, Jade Copet, Yossi Adi, Wei-Ning Hsu, Ali Elkahky, Paden Tomasello, Robin Algayres, Benoît Sagot, Abdelrahman Mohamed, Emmanuel Dupoux

Dialogue Systems & Conversational Agents

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Outcome:	The proposed model reproduces more naturalistic and fluid turn taking than a text-based cascaded model.

MSc-SQL: Multi-Sample Critiquing Small Language Models For Text-To-SQL Translation (2025.naacl-long)

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Satya Krishna Gorti, Ilan Gofman, Zhaoyan Liu, Jiapeng Wu, Noël Vouitsis, Guangwei Yu, Jesse C. Cresswell, Rasa Hosseinzadeh

Text Generation

Challenge:	Recent advances in text-to-SQL generation rely on large closed-source models that present challenges in accessibility, privacy, and latency.
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