# Explaining Length Bias in LLM-Based Preference Evaluations

arXiv: https://arxiv.org/abs/2407.01085 · HTML: https://mlsanity.com/paper/2407.01085/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning](https://mlsanity.com/paper/2308.12032/) (2023)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [MoDS: Model-oriented Data Selection for Instruction Tuning](https://mlsanity.com/paper/2311.15653/) (2023)
- [Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning](https://mlsanity.com/paper/2402.04833/) (2024)
- [Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2310.05199/) (2023)
- [Offline Training of Language Model Agents with Functions as Learnable Weights](https://mlsanity.com/paper/2402.11359/) (2024)
- [Adaptive In-conversation Team Building for Language Model Agents](https://mlsanity.com/paper/2405.19425/) (2024)
- [Spurious Correlations in Reference-Free Evaluation of Text Generation](https://mlsanity.com/paper/2204.09890/) (2022)
- [DQI: Measuring Data Quality in NLP](https://mlsanity.com/paper/2005.00816/) (2020)
- [RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems](https://mlsanity.com/paper/2403.06465/) (2024)
- [Koala: An Index for Quantifying Overlaps with Pre-training Corpora](https://mlsanity.com/paper/2303.14770/) (2023)
- [RECOST: External Knowledge Guided Data-efficient Instruction Tuning](https://mlsanity.com/paper/2402.17355/) (2024)
- [RecExplainer: Aligning Large Language Models for Explaining Recommendation Models](https://mlsanity.com/paper/2311.10947/) (2023)
- [NLPBench: Evaluating Large Language Models on Solving NLP Problems](https://mlsanity.com/paper/2309.15630/) (2023)
- [Better Explain Transformers by Illuminating Important Information](https://mlsanity.com/paper/2401.09972/) (2024)
- [Aligning Language Models for Versatile Text-based Item Retrieval](https://mlsanity.com/paper/2402.18899/) (2024)

## Built on by (most cited first, 30 of 49)

- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [JudgeBench: A Benchmark for Evaluating LLM-based Judges](https://mlsanity.com/paper/2410.12784/) (2024)
- [FlipAttack: Jailbreak LLMs via Flipping](https://mlsanity.com/paper/2410.02832/) (2024)
- [Efficient Inference for Large Reasoning Models: A Survey](https://mlsanity.com/paper/2503.23077/) (2025)
- [The Hallucination Tax of Reinforcement Finetuning](https://mlsanity.com/paper/2505.13988/) (2025)
- [GVPO: Group Variance Policy Optimization for Large Language Model Post-Training](https://mlsanity.com/paper/2504.19599/) (2025)
- [Beyond the Surface: Measuring Self-Preference in LLM Judgments](https://mlsanity.com/paper/2506.02592/) (2025)
- [Assessing Judging Bias in Large Reasoning Models: An Empirical Study](https://mlsanity.com/paper/2504.09946/) (2025)
- [HealthBench Professional: Evaluating Large Language Models on Real Clinician Chats](https://mlsanity.com/paper/2604.27470/) (2026)
- [Adversarial Paraphrasing: A Universal Attack for Humanizing AI-Generated Text](https://mlsanity.com/paper/2506.07001/) (2025)
- [ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking](https://mlsanity.com/paper/2601.06487/) (2026)
- [λ-GRPO: Unifying the GRPO Frameworks with Learnable Token Preferences](https://mlsanity.com/paper/2510.06870/) (2025)
- [Quantifying and Mitigating Self-Preference Bias of LLM Judges](https://mlsanity.com/paper/2604.22891/) (2026)
- [Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities](https://mlsanity.com/paper/2507.13158/) (2025)
- [Think Twice: Branch-and-Rethink Reasoning Reward Model](https://mlsanity.com/paper/2510.23596/) (2025)
- [SSR: Socratic Self-Refine for Large Language Model Reasoning](https://mlsanity.com/paper/2511.10621/) (2025)
- [Adversarial Attacks on LLM-as-a-Judge Systems: Insights from Prompt Injections](https://mlsanity.com/paper/2504.18333/) (2025)
- [Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction](https://mlsanity.com/paper/2607.12835/) (2026)
- [Building Benchmarks from the Ground Up: Community-Centered Evaluation of LLMs in Healthcare Chatbot Settings](https://mlsanity.com/paper/2509.24506/) (2025)
- [Do Chatbot LLMs Talk Too Much? The YapBench Benchmark](https://mlsanity.com/paper/2601.00624/) (2026)
- [EMPA: Evaluating Persona-Aligned Empathy as a Process](https://mlsanity.com/paper/2603.00552/) (2026)
- [Decision-Making with Deliberation: Meta-reviewing as a Document-grounded Dialogue](https://mlsanity.com/paper/2508.05283/) (2025)
- [LLMs Can Get "Brain Rot": A Pilot Study on Twitter/X](https://mlsanity.com/paper/2510.13928/) (2025)
- [One Bias After Another: Mechanistic Reward Shaping and Persistent Biases in Language Reward Models](https://mlsanity.com/paper/2603.03291/) (2026)
- [CoAct: Co-Active LLM Preference Learning with Human-AI Synergy](https://mlsanity.com/paper/2604.17501/) (2026)
- [When Vision-Language Models Judge Without Seeing: Exposing Informativeness Bias](https://mlsanity.com/paper/2604.17768/) (2026)
- [Beyond Task-Oriented and Chitchat Dialogues: Proactive and Transition-Aware Conversational Agents](https://mlsanity.com/paper/2511.08835/) (2025)
- [When More Words Say Less: Decoupling Length and Specificity in Image Description Evaluation](https://mlsanity.com/paper/2601.04609/) (2026)
- [Designing Reliable LLM-Assisted Rubric Scoring for Constructed Responses: Evidence from Physics Exams](https://mlsanity.com/paper/2604.12227/) (2026)
- [From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation](https://mlsanity.com/paper/2606.13221/) (2026)

## Abstract (arXiv)

The use of large language models (LLMs) as judges, particularly in preference comparisons, has become widespread, but this reveals a notable bias towards longer responses, undermining the reliability of such evaluations. To better understand such bias, we propose to decompose the preference evaluation metric, specifically the win rate, into two key components: desirability and information mass, where the former is length-independent and related to trustworthiness such as correctness, toxicity, and consistency, and the latter is length-dependent and represents the amount of information in the response. We empirically demonstrated the decomposition through controlled experiments and found that response length impacts evaluations by influencing information mass. To derive a reliable evaluation metric that assesses content quality without being confounded by response length, we propose AdapAlpaca, a simple yet effective adjustment to win rate measurement. Specifically, AdapAlpaca ensures a fair comparison of response quality by aligning the lengths of reference and test model responses under equivalent length intervals.