Update README.md

README.md

@@ -132,8 +132,10 @@ My view on LLM Evaluation: [Deck](LLMEvaluation.pdf), and [SF Big Analytics and
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 ### Evaluation of evaluation, Evaluation theory, evaluation methods, analysis of evaluation
 - Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks, May 2024, ICML 2024, [arxiv](https://arxiv.org/abs/2405.01719)
+- A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations, EMNLP 2024, [ACLAnthology](https://aclanthology.org/2024.emnlp-main.764/)
 - Ranking Unraveled: Recipes for LLM Rankings in Head-to-Head AI Combat, Nov 2024, [arxiv](https://arxiv.org/abs/2411.14483)
 - Sabotage Evaluations for Frontier Models, Anthropic, Nov 2024, [paper](https://assets.anthropic.com/m/377027d5b36ac1eb/original/Sabotage-Evaluations-for-Frontier-Models.pdf)  [blog post](https://www.anthropic.com/research/sabotage-evaluations)
+- AI Benchmarks and Datasets for LLM Evaluation, Dec 2024, [arxiv](https://arxiv.org/abs/2412.01020), a survey of many LLM becnmarks
 - Lessons from the Trenches on Reproducible Evaluation of Language Models, May 2024, [arxiv](https://arxiv.org/abs/2405.14782)
 - Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks, Aug 2024, [ACL 2024](https://aclanthology.org/2024.acl-long.560/)
 - *Synthetic data in evaluation*, see Chapter 3 in Best Practices and Lessons Learned on Synthetic Data for Language Models, Apr 2024, [arxiv](https://arxiv.org/abs/2404.07503)
@@ -301,6 +303,7 @@ QA is used in many vertical domains, see Vertical section bellow
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 ### Instruction Following
 - Evaluating Large Language Models at Evaluating Instruction Following Oct 2023, [arxiv](https://arxiv.org/abs/2310.07641)
+- CFBench: A Comprehensive Constraints-Following Benchmark for LLMs. Aug 2024, [arxiv](https://arxiv.org/abs/2408.01122)
 - Instruction-Following Evaluation for Large Language Models, IFEval, Nov 2023, [arxiv](https://arxiv.org/abs/2311.07911)
 - FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets, Jul 2023, [arxiv](https://arxiv.org/abs/2307.10928) , [FLASK dataset](https://github.com/kaistAI/FLASK)
 - DINGO: Towards Diverse and Fine-Grained Instruction-Following Evaluation, Mar 2024, [aaai](https://ojs.aaai.org/index.php/AAAI/article/view/29768), [pdf](https://ojs.aaai.org/index.php/AAAI/article/view/29768/31322)