# EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees

arXiv: https://arxiv.org/abs/2503.08893 · HTML: https://mlsanity.com/paper/2503.08893/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 61 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Builds on (most cited first, 30 of 44)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence](https://mlsanity.com/paper/2401.14196/) (2024)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://mlsanity.com/paper/2411.15124/) (2024)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks](https://mlsanity.com/paper/2204.07705/) (2022)
- [AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback](https://mlsanity.com/paper/2305.14387/) (2023)
- [WildChat: 1M ChatGPT Interaction Logs in the Wild](https://mlsanity.com/paper/2405.01470/) (2024)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation](https://mlsanity.com/paper/2211.11501/) (2022)
- [Evaluating Large Language Models at Evaluating Instruction Following](https://mlsanity.com/paper/2310.07641/) (2023)
- [MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues](https://mlsanity.com/paper/2402.14762/) (2024)
- [Domino: Discovering Systematic Errors with Cross-Modal Embeddings](https://mlsanity.com/paper/2203.14960/) (2022)
- [MathScale: Scaling Instruction Tuning for Mathematical Reasoning](https://mlsanity.com/paper/2403.02884/) (2024)
- [DART-Math: Difficulty-Aware Rejection Tuning for Mathematical Problem-Solving](https://mlsanity.com/paper/2407.13690/) (2024)
- [Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation](https://mlsanity.com/paper/2402.11443/) (2024)
- [Clio: Privacy-Preserving Insights into Real-World AI Use](https://mlsanity.com/paper/2412.13678/) (2024)
- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [Goal-Driven Explainable Clustering via Language Descriptions](https://mlsanity.com/paper/2305.13749/) (2023)
- [Describing Differences between Text Distributions with Natural Language](https://mlsanity.com/paper/2201.12323/) (2022)
- [The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions](https://mlsanity.com/paper/2310.12418/) (2023)
- [Adaptive Testing of Computer Vision Models](https://mlsanity.com/paper/2212.02774/) (2022)

## Built on by (most cited first, 27 of 27)

- [Reinforcement Learning for Reasoning in Large Language Models with One Training Example](https://mlsanity.com/paper/2504.20571/) (2025)
- [RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments](https://mlsanity.com/paper/2511.07317/) (2025)
- [What Prompts Don't Say: Understanding and Managing Underspecification in LLM Prompts](https://mlsanity.com/paper/2505.13360/) (2025)
- [What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data](https://mlsanity.com/paper/2510.26202/) (2025)
- [The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think](https://mlsanity.com/paper/2505.10185/) (2025)
- [Discovering Knowledge Deficiencies of Language Models on Massive Knowledge Base](https://mlsanity.com/paper/2503.23361/) (2025)
- [ErrorMap and ErrorAtlas: Charting the Failure Landscape of Large Language Models](https://mlsanity.com/paper/2601.15812/) (2026)
- [AdaptMI: Adaptive Skill-based In-context Math Instruction for Small Language Models](https://mlsanity.com/paper/2505.00147/) (2025)
- [Skill-Aware Data Selection and Fine-Tuning for Data-Efficient Reasoning Distillation](https://mlsanity.com/paper/2601.10109/) (2026)
- [Automated Capability Evaluation of Foundation Models](https://mlsanity.com/paper/2505.17228/) (2025)
- [CLEAR: Error Analysis via LLM-as-a-Judge Made Easy](https://mlsanity.com/paper/2507.18392/) (2025)
- [Evalet: Evaluating Large Language Models through Functional Fragmentation](https://mlsanity.com/paper/2509.11206/) (2025)
- [Explainable Model Routing for Agentic Workflows](https://mlsanity.com/paper/2604.03527/) (2026)
- [SCAN: Structured Capability Assessment and Navigation for LLMs](https://mlsanity.com/paper/2505.06698/) (2025)
- [Data Swarms: Optimizable Generation of Synthetic Evaluation Data](https://mlsanity.com/paper/2506.00741/) (2025)
- [Trust by Design: Skill Profiles for Transparent, Cost-Aware LLM Routing](https://mlsanity.com/paper/2602.02386/) (2026)
- [MINERVA-Cultural: A Benchmark for Cultural and Multilingual Long Video Reasoning](https://mlsanity.com/paper/2601.10649/) (2026)
- [BenchBrowser: Retrieving Evidence for Evaluating Benchmark Validity](https://mlsanity.com/paper/2603.18019/) (2026)
- [RouteProfile: Graph-Based Profiling for Cold-Start LLM Routing](https://mlsanity.com/paper/2605.00180/) (2026)
- [Learning from Synthetic Data without Model Collapse in Iterative Instruction Tuning](https://mlsanity.com/paper/2607.17043/) (2026)
- [The Flaw of Averages: Measuring Benchmark-Level Distributional Robustness](https://mlsanity.com/paper/2509.25671/) (2025)
- [Probing the Knowledge Boundary: An Interactive Agentic Framework for Deep Knowledge Extraction](https://mlsanity.com/paper/2602.00959/) (2026)
- [PROMPT2BOX:Improving LLM Weakness Discovery and Specificity Estimation by Uncovering Entailment Structure among Prompts](https://mlsanity.com/paper/2603.21438/) (2026)
- [Characterizing Model-Native Skills](https://mlsanity.com/paper/2604.17614/) (2026)
- [Evolutionary Task Discovery: Advancing Reasoning Frontiers via Skill Composition and Complexity Scaling](https://mlsanity.com/paper/2605.11666/) (2026)
- [CRAFT: Clustering Rubrics to Diagnose Weak LLM Capabilities and Generate Targeted Fine-Tuning Data](https://mlsanity.com/paper/2607.16122/) (2026)
- [The Value of Being Wrong: Self-Mined Visual In-Context Learning from Errors](https://mlsanity.com/paper/2610.16977/) (2026)

## Abstract (arXiv)

An ideal model evaluation should achieve two goals: identifying where the model fails and providing actionable improvement guidance. Toward these goals for language model (LM) evaluations, we formulate the problem of generating a weakness profile, a set of weaknesses expressed in natural language, given an LM's performance on every individual instance in a benchmark. We introduce a suite of quantitative assessments to compare different weakness profiling methods. We also introduce a weakness profiling method EvalTree. EvalTree constructs a capability tree where each node represents a capability described in natural language and is linked to a subset of benchmark instances that specifically evaluate this capability; it then extracts nodes where the LM performs poorly to generate a weakness profile. On the MATH and WildChat benchmarks, we show that EvalTree outperforms baseline weakness profiling methods by identifying weaknesses more precisely and comprehensively. Weakness profiling further enables weakness-guided data collection, and training data collection guided by EvalTree-identified weaknesses improves LM performance more than other data collection strategies. We also show how EvalTree exposes flaws in Chatbot Arena's human-voter-based evaluation practice. To facilitate future work, we provide an interface that allows practitioners to interactively explore the capability trees built by EvalTree.