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HELM: Holistic Evaluation of Language Models
This episode explores the HELM framework for evaluating language models, arguing that once models become general-purpose infrastructure, single-dataset accuracy benchmarks are too narrow to capture their real-world behavior. It explains how HELM organizes evaluation across 30 models, 16 core scenarios, and seven metric families, measuring not just accuracy but also calibration, robustness, fairness, bias, toxicity, and efficiency under standardized conditions. The discussion highlights why HELM’s scenario-by-metric grid and targeted side studies on issues like reasoning, memorization, copyright, and disinformation matter: they make gaps in measurement visible instead of hiding them behind a single leaderboard score. A listener would find it interesting because it shows how benchmark design reflects values, and why model rankings can be misleading if they ignore confidence, harm, and cost.
Sources:
1. HELM: Holistic Evaluation of Language Models
https://arxiv.org/pdf/2211.09110
2. Equality of Opportunity in Supervised Learning — Moritz Hardt, Eric Price, Nathan Srebro, 2016
https://arxiv.org/abs/1610.02413
3. Language (Technology) is Power: A Critical Survey of "Bias" in NLP — Su Lin Blodgett, Solon Barocas, Hal Daume III, Hanna Wallach, 2020
https://arxiv.org/abs/2005.14050
4. StereoSet: Measuring stereotypical bias in pretrained language models — Moin Nadeem, Anna Bethke, Siva Reddy, 2020
https://arxiv.org/abs/2004.09456
5. BBQ: A Hand-Built Bias Benchmark for Question Answering — Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, Samuel R. Bowman, 2021
https://arxiv.org/abs/2110.08193
6. Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification — Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, Lucy Vasserman, 2019
https://arxiv.org/abs/1903.04561
7. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models — Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, Noah A. Smith, 2020
https://arxiv.org/abs/2009.11462
8. Challenges in Detoxifying Language Models — Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang, 2021
https://arxiv.org/abs/2109.07445
9. ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection — Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar, 2022
https://arxiv.org/abs/2203.09509
10. On the Opportunities and Risks of Foundation Models — Rishi Bommasani et al., 2021
https://scholar.google.com/scholar?q=On+the+Opportunities+and+Risks+of+Foundation+Models
11. The EleutherAI Language Model Evaluation Harness — Leo Gao et al., 2021
https://scholar.google.com/scholar?q=The+EleutherAI+Language+Model+Evaluation+Harness
12. Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models — Aarohi Srivastava et al., 2022
https://scholar.google.com/scholar?q=Beyond+the+Imitation+Game%3A+Quantifying+and+Extrapolating+the+Capabilities+of+Language+Models
13. Dynabench: Rethinking Benchmarking in NLP — Douwe Kiela et al., 2021
https://scholar.google.com/scholar?q=Dynabench%3A+Rethinking+Benchmarking+in+NLP
14. What Will it Take to Fix Benchmarking in Natural Language Understanding? — Samuel R. Bowman, George Dahl, 2021
https://scholar.google.com/scholar?q=What+Will+it+Take+to+Fix+Benchmarking+in+Natural+Language+Understanding%3F
15. Rethinking Benchmark and Contamination for Language Models with Rephrased Samples — Shuo Yang et al., 2023
https://scholar.google.com/scholar?q=Rethinking+Benchmark+and+Contamination+for+Language+Models+with+Rephrased+Samples
16. Investigating Data Contamination in Modern Benchmarks for Large Language Models — Chunyuan Deng et al., 2023
https://scholar.google.com/scholar?q=Investigating+Data+Contamination+in+Modern+Benchmarks+for+Large+Language+Models
17. Benchmark Data Contamination of Large Language Models: A Survey — Cheng Xu et al., 2024
https://scholar.google.com/scholar?q=Benchmark+Data+Contamination+of+Large+Language+Models%3A+A+Survey
18. Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead — Vidhisha Balachandran et al., 2025
https://scholar.google.com/scholar?q=Inference-Time+Scaling+for+Complex+Tasks%3A+Where+We+Stand+and+What+Lies+Ahead
19. WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models — Kangyun Ning et al., 2024
https://scholar.google.com/scholar?q=WTU-EVAL%3A+A+Whether-or-Not+Tool+Usage+Evaluation+Benchmark+for+Large+Language+Models
20. T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step — Zehui Chen et al., 2023
https://scholar.google.com/scholar?q=T-Eval%3A+Evaluating+the+Tool+Utilization+Capability+of+Large+Language+Models+Step+by+Step
21. AI Post Transformers: IMO-Bench for Robust Mathematical Reasoning — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-imo-bench-for-robust-mathematical-reason-143489.mp3
22. AI Post Transformers: Real Context Size and Context Rot — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-07-real-context-size-and-context-rot-56cbb4.mp3
23. AI Post Transformers: Qwen3Guard: Streaming Three-Way Safety Classification for LLMs — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-03-16-qwen3guard-streaming-three-way-safety-cl-26b0ef.mp3
24. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3
Interactive Visualization: HELM: Holistic Evaluation of Language Models
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By mcgrofThis episode explores the HELM framework for evaluating language models, arguing that once models become general-purpose infrastructure, single-dataset accuracy benchmarks are too narrow to capture their real-world behavior. It explains how HELM organizes evaluation across 30 models, 16 core scenarios, and seven metric families, measuring not just accuracy but also calibration, robustness, fairness, bias, toxicity, and efficiency under standardized conditions. The discussion highlights why HELM’s scenario-by-metric grid and targeted side studies on issues like reasoning, memorization, copyright, and disinformation matter: they make gaps in measurement visible instead of hiding them behind a single leaderboard score. A listener would find it interesting because it shows how benchmark design reflects values, and why model rankings can be misleading if they ignore confidence, harm, and cost.
Sources:
1. HELM: Holistic Evaluation of Language Models
https://arxiv.org/pdf/2211.09110
2. Equality of Opportunity in Supervised Learning — Moritz Hardt, Eric Price, Nathan Srebro, 2016
https://arxiv.org/abs/1610.02413
3. Language (Technology) is Power: A Critical Survey of "Bias" in NLP — Su Lin Blodgett, Solon Barocas, Hal Daume III, Hanna Wallach, 2020
https://arxiv.org/abs/2005.14050
4. StereoSet: Measuring stereotypical bias in pretrained language models — Moin Nadeem, Anna Bethke, Siva Reddy, 2020
https://arxiv.org/abs/2004.09456
5. BBQ: A Hand-Built Bias Benchmark for Question Answering — Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, Samuel R. Bowman, 2021
https://arxiv.org/abs/2110.08193
6. Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification — Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, Lucy Vasserman, 2019
https://arxiv.org/abs/1903.04561
7. RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models — Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, Noah A. Smith, 2020
https://arxiv.org/abs/2009.11462
8. Challenges in Detoxifying Language Models — Johannes Welbl, Amelia Glaese, Jonathan Uesato, Sumanth Dathathri, John Mellor, Lisa Anne Hendricks, Kirsty Anderson, Pushmeet Kohli, Ben Coppin, Po-Sen Huang, 2021
https://arxiv.org/abs/2109.07445
9. ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection — Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar, 2022
https://arxiv.org/abs/2203.09509
10. On the Opportunities and Risks of Foundation Models — Rishi Bommasani et al., 2021
https://scholar.google.com/scholar?q=On+the+Opportunities+and+Risks+of+Foundation+Models
11. The EleutherAI Language Model Evaluation Harness — Leo Gao et al., 2021
https://scholar.google.com/scholar?q=The+EleutherAI+Language+Model+Evaluation+Harness
12. Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models — Aarohi Srivastava et al., 2022
https://scholar.google.com/scholar?q=Beyond+the+Imitation+Game%3A+Quantifying+and+Extrapolating+the+Capabilities+of+Language+Models
13. Dynabench: Rethinking Benchmarking in NLP — Douwe Kiela et al., 2021
https://scholar.google.com/scholar?q=Dynabench%3A+Rethinking+Benchmarking+in+NLP
14. What Will it Take to Fix Benchmarking in Natural Language Understanding? — Samuel R. Bowman, George Dahl, 2021
https://scholar.google.com/scholar?q=What+Will+it+Take+to+Fix+Benchmarking+in+Natural+Language+Understanding%3F
15. Rethinking Benchmark and Contamination for Language Models with Rephrased Samples — Shuo Yang et al., 2023
https://scholar.google.com/scholar?q=Rethinking+Benchmark+and+Contamination+for+Language+Models+with+Rephrased+Samples
16. Investigating Data Contamination in Modern Benchmarks for Large Language Models — Chunyuan Deng et al., 2023
https://scholar.google.com/scholar?q=Investigating+Data+Contamination+in+Modern+Benchmarks+for+Large+Language+Models
17. Benchmark Data Contamination of Large Language Models: A Survey — Cheng Xu et al., 2024
https://scholar.google.com/scholar?q=Benchmark+Data+Contamination+of+Large+Language+Models%3A+A+Survey
18. Inference-Time Scaling for Complex Tasks: Where We Stand and What Lies Ahead — Vidhisha Balachandran et al., 2025
https://scholar.google.com/scholar?q=Inference-Time+Scaling+for+Complex+Tasks%3A+Where+We+Stand+and+What+Lies+Ahead
19. WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models — Kangyun Ning et al., 2024
https://scholar.google.com/scholar?q=WTU-EVAL%3A+A+Whether-or-Not+Tool+Usage+Evaluation+Benchmark+for+Large+Language+Models
20. T-Eval: Evaluating the Tool Utilization Capability of Large Language Models Step by Step — Zehui Chen et al., 2023
https://scholar.google.com/scholar?q=T-Eval%3A+Evaluating+the+Tool+Utilization+Capability+of+Large+Language+Models+Step+by+Step
21. AI Post Transformers: IMO-Bench for Robust Mathematical Reasoning — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-imo-bench-for-robust-mathematical-reason-143489.mp3
22. AI Post Transformers: Real Context Size and Context Rot — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-07-real-context-size-and-context-rot-56cbb4.mp3
23. AI Post Transformers: Qwen3Guard: Streaming Three-Way Safety Classification for LLMs — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-03-16-qwen3guard-streaming-three-way-safety-cl-26b0ef.mp3
24. AI Post Transformers: Speculative Decoding in Real vLLM Serving — Hal Turing & Dr. Ada Shannon, 2026
https://podcast.do-not-panic.com/episodes/2026-04-04-speculative-decoding-in-real-vllm-servin-6f4e2b.mp3
Interactive Visualization: HELM: Holistic Evaluation of Language Models