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arXiv AI

Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning

The study investigates how fine‑tuning a large language model on a single task‑language pair influences performance on other task‑language pairs. Using LoRA fine‑tuning across multiple open‑weight LLM families, 11 languages, and four benchmarks, the authors decompose transfer into matched‑task, cross‑task, and cross‑task cross‑language regimes. They find that while single‑source fine‑tuning generally improves performance, the gains are highly asymmetric, with matched‑task cross‑language transfer being most effective and driven mainly by the target language rather than model architecture.

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Sep 4

One Model to Translate Them All? A Journey to Mount Doom for Multilingual Model Merging

The paper investigates weight‑space merging of independently fine‑tuned multilingual machine translation models. Experiments show that merging is more successful when models share a target language, yet it still cannot match the peak performance of language‑specific checkpoints. When target languages differ, performance drops sharply, and analysis reveals that overlapping neuron activation and incompatible upper‑layer geometries cause these failures.

By Baban Gain, Trilok Nath Singh, Asif Ekbal
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arXiv AI
Sep 21

Decoupling Internal Representational Changes and Causal Importance in Fine-Tuned Large Language Models

Fine‑tuning reshapes internal representations of large language models, affecting attention patterns and layer‑wise activations. The study shows that components identified by EAP as important for task performance cluster in specific layers, yet these layers do not align with those undergoing the largest representational changes. Additionally, overlapping EAP components across different tasks do not guarantee cross‑task transfer and can even degrade performance when tasks differ in nature.

By Lingfang Li, Procheta Sen, Shubham Das, Danushka Bollegala
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arXiv Computation and Language
Aug 27

Lower-Resource, Higher Scores: Language Bias in LLM Evaluators

The paper demonstrates that large language model (LLM) evaluators, whether reward‑model based or prompted LLM‑as‑a‑Judge, exhibit significant language bias in multilingual settings. Experiments with semantically identical instruction‑response pairs across 23 languages reveal that lower‑resource languages receive higher scores, a bias that persists across eight open‑weight evaluators and is not detectable by standard pairwise accuracy metrics. The authors link the bias to model uncertainty and language identity, showing it cannot be explained by content difficulty alone.

By Ej Zhou, Lucas Resck, Zheng Hui, Anna Korhonen
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arXiv Machine Learning
5d ago

New LoRA Skills Should Read but Never Write

The paper introduces READ, a method for composing low‑rank adapters (LoRA) in large language models. By rewriting each adapter into a balanced canonical form and enforcing a one‑directional coupling, READ allows new skills to read but never write into the output subspaces of existing skills, eliminating interference. Experiments on four benchmark suites and two model families show that READ consistently outperforms existing baselines, improving SuperGLUE scores by over twenty points and domain suite scores by more than seven points.

By Zeyan Li, Panqi Yang, Qirong Guo, Shengda Zhuo, SIyuan Qiu, Hu Xu, Chun Li, Jianfeng Xu
llmsfine-tuningbenchmarks
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