arXiv AI
Jun 30
Aligning Language Model Benchmarks with Pairwise Preferences
arXiv:2602. 02898v3 Announce Type: replace Abstract: Language model benchmarks are pervasive and computationally-efficient proxies for real-world performance.
D2VBench: Benchmarking Large Language Models with Value Dilemmas in Daily Scenarios
With the wide application of large language models (LLMs) in real-world scenarios, the value implication of their outputs is crucial. However, existing evaluation benchmarks suffer from insufficient coverage of value dilemmas in daily scenarios involving multiple value conflicts and simplistic evaluation formalisms that fail to assess LLMs' value alignment.
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arXiv AI
Jun 10
RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty
arXiv:2602. 12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field.
arXiv AI
Aug 21
DiverValue-Bench: A Benchmark and Fine-Tuning Framework for Aligning Large Language Models with Diverse Human Values
arXiv:2509. 08022v3 Announce Type: replace-cross Abstract: Aligning large language models (LLMs) with diverse human values is essential for safe and effective deployment, yet existing benchmarks often overlook cultural and demographic variation.
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.
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 Computation and Language
6d ago
Large Language Model Selection with Limited Annotations
arXiv:2605.24981v2 Announce Type: replace Abstract: Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotati...
Hugging Face Trending Papers
Jul 23
When Trivia Is Not Trivial: Everyday Knowledge Failures in Multilingual LLMs
Quiz rooms, trivia nights, and quiz shows challenge human knowledge across a wide range of topics, from canonical facts to everyday culture. In this paper, we examine whether large language models (LLMs) can perform competitively in such settings, using quiz-style questions to test them on both common and niche topics.
arXiv AI
4d ago
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 Computation and Language
Aug 27
IDEAlign: Comparing Ideas of Large Language Models to Domain Expert
IDEAlign introduces a new protocol for evaluating the similarity of large language model (LLM) annotations to expert judgments. It uses pick‑the‑odd‑one‑out tasks to capture expert similarity and benchmarks various similarity methods—including text embeddings, topic models, and LLM-as-a-judge—against these human ratings. Applied to educational datasets, the study finds that most metrics miss nuanced expert dimensions, with LLM-as-a-judge performing best yet still insufficient for full expert alignment.
arXiv AI
Jun 26
NuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language Models
arXiv:2606. 27047v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong performance across a wide range of tasks, but ensuring their reliability in highly technical domains remains a significant challenge.
arXiv AI
Sep 10
Same Values, Different Languages? From Multilingual Probing to Steering LLMs Toward Chinese Social Values
arXiv:2609.08515v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly integrated into human society, aligning them with pluralistic social values has become a critical pr...
arXiv AI
Jun 2
Distributional Open-Ended Evaluation of LLM Cultural Value Alignment Based on Value Codebook
arXiv:2604. 06210v3 Announce Type: replace-cross Abstract: As LLMs are globally deployed, aligning their cultural value orientations is critical for safety and user engagement.