ALOPE: Adaptive Layer Optimization for Translation Quality Estimation using Large Language Models

• URL: http://arxiv.org/abs/2508.07484v1
• Date: Sun, 10 Aug 2025 20:59:44 GMT
• Title: ALOPE: Adaptive Layer Optimization for Translation Quality Estimation using Large Language Models
• Abstract summary: Large Language Models (LLMs) have shown remarkable performance across a wide range of natural language processing tasks.<n>QE for Machine Translation (MT) assesses the quality of a source-target pair without relying on reference translations.<n>This paper introduces ALOPE, an adaptive layer-optimization framework designed to enhance LLM-based QE.
• Score: 8.518082319087375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have shown remarkable performance across a wide range of natural language processing tasks. Quality Estimation (QE) for Machine Translation (MT), which assesses the quality of a source-target pair without relying on reference translations, remains a challenging cross-lingual task for LLMs. The challenges stem from the inherent limitations of existing LLM-based QE systems, which are pre-trained for causal language modelling rather than regression-specific tasks, further elevated by the presence of low-resource languages given pre-training data distribution. This paper introduces ALOPE, an adaptive layer-optimization framework designed to enhance LLM-based QE by restructuring Transformer representations through layer-wise adaptation for improved regression-based prediction. Our framework integrates low-rank adapters (LoRA) with regression task heads, leveraging selected pre-trained Transformer layers for improved cross-lingual alignment. In addition to the layer-specific adaptation, ALOPE introduces two strategies-dynamic weighting, which adaptively combines representations from multiple layers, and multi-head regression, which aggregates regression losses from multiple heads for QE. Our framework shows improvements over various existing LLM-based QE approaches. Empirical evidence suggests that intermediate Transformer layers in LLMs provide contextual representations that are more aligned with the cross-lingual nature of the QE task. We make resultant models and framework code publicly available for further research, also allowing existing LLM-based MT frameworks to be scaled with QE capabilities.

Related papers

• Domain-Specific Quality Estimation for Machine Translation in Low-Resource Scenarios [10.17317882426833]
  We investigate sentence-level QE for English to Indic machine translation across four domains (Healthcare, Legal, Tourism, and General) and five language pairs.<n>Findings indicate that while closed-weight models achieve strong performance via prompting alone, prompt-only approaches remain fragile for open-weight models.
  arXiv  Detail & Related papers  (2026-03-07T22:55:25Z)
• Sample-Efficient Online Learning in LM Agents via Hindsight Trajectory Rewriting [92.57796055887995]
  We introduce ECHO, a prompting framework that adapts hindsight experience replay from reinforcement learning for language model agents.<n> ECHO generates optimized trajectories for alternative goals that could have been achieved during failed attempts.<n>We evaluate ECHO on stateful versions of XMiniGrid, a text-based navigation and planning benchmark, and PeopleJoinQA, a collaborative information-gathering enterprise simulation.
  arXiv  Detail & Related papers  (2025-10-11T18:11:09Z)
• Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey [69.45421620616486]
  This work presents the first structured taxonomy and analysis of discrete tokenization methods designed for large language models (LLMs)<n>We categorize 8 representative VQ variants that span classical and modern paradigms and analyze their algorithmic principles, training dynamics, and integration challenges with LLM pipelines.<n>We identify key challenges including codebook collapse, unstable gradient estimation, and modality-specific encoding constraints.
  arXiv  Detail & Related papers  (2025-07-21T10:52:14Z)
• TALL -- A Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages [13.416341692917676]
  This paper presents TALL (Trainable Architecture for Enhancing LLM Performance in Low-Resource Languages), which integrates an LLM with two bilingual translation models.<n>Our experiments on Hebrew demonstrate significant improvements over several baselines, including direct use, naive translation, and fine-tuning approaches.
  arXiv  Detail & Related papers  (2025-06-05T14:02:12Z)
• Distilling Transitional Pattern to Large Language Models for Multimodal Session-based Recommendation [67.84581846180458]
  Session-based recommendation (SBR) predicts the next item based on anonymous sessions.<n>Recent Multimodal SBR methods utilize simplistic pre-trained models for modality learning but have limitations in semantic richness.<n>We propose a multimodal LLM-enhanced framework TPAD, which extends a distillation paradigm to decouple and align transitional patterns for promoting MSBR.
  arXiv  Detail & Related papers  (2025-04-13T07:49:08Z)
• Bridging the Linguistic Divide: A Survey on Leveraging Large Language Models for Machine Translation [33.08089616645845]
  Large Language Models (LLMs) have reshaped the landscape of machine translation (MT)<n>We analyze techniques such as few-shot prompting, cross-lingual transfer, and parameter-efficient fine-tuning.<n>We discuss persistent challenges - such as hallucinations, evaluation inconsistencies, and inherited biases.
  arXiv  Detail & Related papers  (2025-04-02T17:26:40Z)
• Can Large Language Models Be Trusted as Evolutionary Optimizers for Network-Structured Combinatorial Problems? [8.431866560904753]
  Large Language Models (LLMs) have shown strong capabilities in language understanding and reasoning across diverse domains.<n>In this work, we propose a systematic framework to evaluate the capability of LLMs to engage with problem structures.<n>We adopt the commonly used evolutionary (EVO) and propose a comprehensive evaluation framework that rigorously assesses the output fidelity of LLM-based operators.
  arXiv  Detail & Related papers  (2025-01-25T05:19:19Z)
• LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment [12.80921403367322]
  Large Language Models (LLMs) demonstrate exceptional performance across various domains.<n> Quantization techniques, which reduce the size and memory requirements of LLMs, are effective for deploying LLMs on resource-limited edge devices.<n>We propose Layer-Specific Adaptive Quantization (LSAQ), a system for adaptive quantization and dynamic deployment of LLMs based on layer importance.
  arXiv  Detail & Related papers  (2024-12-24T03:43:15Z)
• Adaptive Pruning for Large Language Models with Structural Importance Awareness [66.2690963378878]
  Large language models (LLMs) have significantly improved language understanding and generation capabilities.<n>LLMs are difficult to deploy on resource-constrained edge devices due to their high computational and storage resource demands.<n>We propose structurally-aware adaptive pruning (SAAP) to significantly reduce the computational and memory costs while maintaining model performance.
  arXiv  Detail & Related papers  (2024-12-19T18:08:04Z)
• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning [53.6472920229013]
  Large Language Models (LLMs) have demonstrated impressive capability in many natural language tasks. LLMs are prone to produce errors, hallucinations and inconsistent statements when performing multi-step reasoning. We introduce Q*, a framework for guiding LLMs decoding process with deliberative planning.
  arXiv  Detail & Related papers  (2024-06-20T13:08:09Z)
• Don't Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation [0.6998085564793366]
  This work introduces QE-fusion, a method that synthesizes translations using a quality estimation metric (QE) We demonstrate that our approach generates novel translations in over half of the cases. We empirically establish that QE-fusion scales linearly with the number of candidates in the pool.
  arXiv  Detail & Related papers  (2024-01-12T16:52:41Z)
• Examining Scaling and Transfer of Language Model Architectures for Machine Translation [51.69212730675345]
  Language models (LMs) process sequences in a single stack of layers, and encoder-decoder models (EncDec) utilize separate layer stacks for input and output processing. In machine translation, EncDec has long been the favoured approach, but with few studies investigating the performance of LMs.
  arXiv  Detail & Related papers  (2022-02-01T16:20:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.