CLaS-Bench: A Cross-Lingual Alignment and Steering Benchmark (2026.findings-acl)
Copied to clipboard
Daniil Gurgurov, Yusser Al Ghussin, Tanja Baeumel, Cheng-Ting Chou, Patrick Schramowski, Marius Mosbach, Josef Van Genabith, Simon Ostermann
| Challenge: | Understanding and controlling behavior of large language models (LLMs) is an important topic in multilingual NLP. |
| Approach: | They propose a lightweight parallel-question benchmark for evaluating language-forcing behavior in large language models across 32 languages. |
| Outcome: | The proposed benchmark measures language steering in 32 languages across 32 languages. |
Similar Papers
STEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models can adapt outputs to align with community-specific norms, perspectives and communication styles. |
| Approach: | They propose a benchmark to assess community-specific steering using contrasting reddit communities. |
| Outcome: | STEER-BENCH assesses how well large language models understand community-specific instructions, their resilience to adversarial steering attempts, and their ability to accurately represent cultural and ideological perspectives. |
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
| Approach: | They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages. |
| Outcome: | Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. |
Evaluating the Impact of SAE-based Language Steering on LLM Performance (2026.eacl-srw)
Copied to clipboard
| Challenge: | Recent advances in Sparse Autoencoders (SAEs) have revealed interpretable features within large language models (LLMs) however, the impact of SAE-based language steering on output quality and task performance remains unclear. |
| Approach: | They apply language-specific SAE feature steering to three LLMs from two model families and evaluate it on a translation task and a multilingual question-answering task. |
| Outcome: | The proposed approach outperforms prompting and language neuron-based steering on translation and multilingual question-answering tasks. |
ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Large Language Models exhibit more language confusion as they become multilingual . authors propose a lightweight approach for reducing language confusion based on language-specific steering vectors . |
| Approach: | They propose a lightweight approach to reduce language confusion by using language-specific steering vectors. |
| Outcome: | The proposed approach reduces language confusion in large language models . it leverages language-specific steering vectors for effective LLM steering . |
From Neurons to Semantics: Evaluating Cross-Linguistic Alignment Capabilities of Large Language Models via Neurons Alignment (2025.acl-long)
Copied to clipboard
| Challenge: | Existing alignment benchmarks focus on sentence embeddings, but prior research has shown that neural models tend to induce a non-smooth representation space, which impact of semantic alignment evaluation on low-resource languages. |
| Approach: | They propose a novel cross-lingual alignment evaluation method based on the consistency of parallel sentences to assess model alignment. |
| Outcome: | The proposed method achieves a correlation of 0.9556 with downstream tasks performance and 0.8524 with transferability even with a small dataset. |
How to Improve LLMs’ Performance on Specific Languages: A Perspective on LLM-Derived Language Similarity (2026.acl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) exhibit uneven performance across languages. |
| Approach: | They propose to use a framework to quantify the similarity within each language pair through both the lenses of language-specific performance patterns and cross-lingual transferability. |
| Outcome: | The proposed approach outperforms traditional linguistic typology and cross-lingual transferability measures on multilingual LLMs. |
Can Activation Steering Generalize Across Languages? A Study on Syllogistic Reasoning in Language Models (2026.eacl-long)
Copied to clipboard
| Challenge: | Prior work has focused on activation steering for Large Language Models (LLMs) this technique can be used to improve reasoning accuracy and transferability across languages. |
| Approach: | They propose to use activation steering to steer models towards a cross-lingual reasoning space. |
| Outcome: | The proposed techniques generalise well to multilingual datasets while minimizing language modelling performance. |
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)
Copied to clipboard
Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou
| Challenge: | Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks. |
| Approach: | They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets . |
| Outcome: | The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages. |
Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data (2025.findings-emnlp)
Copied to clipboard
Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu, Wenyu Zhang, Muhammad Huzaifah, Nattadaporn Lertcheva, Shuo Sun, Nancy F. Chen, Jinyang Wu, AiTi Aw
| Challenge: | Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence. |
| Approach: | They propose a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning . the benchmark includes curated question answering datasets requiring both linguistic and empathetic understanding . |
| Outcome: | The proposed benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent LLMs. |
Soteria: Language-Specific Functional Parameter Steering for Multilingual Safety Alignment (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Soteria locates and minimally adjusts the “functional heads” most responsible for harmful content generation in each language. |
| Approach: | Soteria locates and minimally adjusts the "functional heads" responsible for harmful content generation in each language. |
| Outcome: | The proposed approach reduces harmful content generation in languages while preserving model performance. |