arXiv AI
Mind Which Bird You Favour: Parameterizing Adequacy-Fluency Balance in Meta-Evaluation of Machine Translation
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arXiv AI
Sep 30
PADM\'E: Preference Alignment Data Synthesis for Meta-Evaluation of LM Agent Evaluators
PADM'E is a method for synthesizing preference‑aligned data to meta‑evaluate language‑model (LM) evaluators of agentic behaviors. It reframes meta‑evaluation as a preference judgment problem, generating criterion‑based data with small LMs and no human involvement. In a prototype, PADM'E produced 1,000 samples across four domains and three criteria, and human validation showed agreement with human judgment rising from 73% to 85% compared to a naive baseline.
arXiv AI
Jul 7
Safe Inference-Time Alignment via Lagrangian Reward Augmentation
arXiv:2607. 02781v1 Announce Type: cross Abstract: Inference-time alignment steers a frozen language model during decoding using auxiliary reward signals, avoiding the cost of repeated weight updates.
arXiv AI
Sep 25
Tag-Aware Structured Text Translation: Towards a Systematic Understanding
The paper introduces a systematic approach to tag-aware translation, addressing the trade-off between structural tag diversity and translation naturalness in synthetic data. It proposes a hybrid synthesis strategy (Hy‑LST) and a multi‑task fine‑tuning framework that decomposes translation into four sub‑tasks. Additionally, it employs a group relative policy optimization with three reward functions—fluency, tag fidelity, and tag‑scoped translation quality—to jointly optimize these objectives, achieving superior performance across six language pairs.
arXiv Computation and Language
Sep 21
Cultural Alignment in Large Language Models Using Soft Prompt Tuning
The paper proposes a method for culturally aligning large language models (LLMs) using soft prompt tuning optimized via Differential Evolution (DE). Unlike traditional fine‑tuning or reinforcement learning, this approach keeps model weights frozen and requires no preference data, instead leveraging aggregated survey scores from Hofstede's Value Survey Module (VSM13). Experiments on four countries and four instruction‑tuned models show that DE‑optimized prompts reduce cultural discrepancy, improve agreement with the World Values Survey, and are preferred in blinded pairwise evaluations by LLM judges.
arXiv AI
Aug 14
Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds
arXiv:2608. 13069v1 Announce Type: new Abstract: Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants.
arXiv AI
Jun 8
VALUEFLOW: Toward Pluralistic and Steerable Value-based Alignment in Large Language Models
arXiv:2602. 03160v2 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with the diverse spectrum of human values remains a central challenge: preference-based methods often fail to capture deeper motivational principles.