ARelated Work1 Data Attribution Evaluation: Given recent developments in data attribution methods for LLMs,2 past works in evaluating these methods fall two major categories: leave-out-out and task-based3 evaluation. Leave-one-out evaluation measures the correlation between the data attribution method4 scores and model-retraining, which can also be approximated using linear datamodeling score [26].5 In task-based evaluation, the data attribution method is evaluated based on its application towards6 downstream task, such as noisy label detection, counterfactual evaluation [3, 13].7 Training Data Selection: Selecting high-quality training data selection is important for efficient8 learning in LLMs. Common approaches to data selection relies on heuristic filtering, such as de-9 duplication and lexicon-filtering, [34], or semantic rating [48, 52]. Recent works have applied data10 attribution methods towards data selection in LLMs in both pre-training [56, 59, 15] and post-training11 [45, 53, 31]. These data attribution methods are dynamic and model-aware - increasing the frequency12 of performing selection is one way to take greater account for group influence, where online selection13 at each training step is most fine-grained [49].14 Toxicity/Bias Detection: Detecting and mitigating toxic/biased LLMs outputs is a crucial for safe15 deployment in real-word settings. Existing methods for detecting toxicity/bias in LLMs commonly16 include online API tools 1 [37] or LLM-classifiers [58, 21, 16, 27]. Factual Attribution: Identifying training examples which causes LLMs to generate specific factual20 statements is an important application of data attribution as AI tools are becoming increasingly21 common. Apart from baseline retrieval methods that leverage lexical/semantic similarity like BM2522 [48], Rep Sim [44] and Gecko [33], recent works have explored the use of data attribution in tracing23 factual knowledge in both pre-training[6] and post-training [42, 2].24 We provide below descriptions to the data attribution methods and non-attribution baselines evaluated26 in this work. Note that in our work, we consider non-attribution baselines as methods that do not27 estimate the impact of training samples on models, as detailed in [19].28 Rep-Sim [44]: (Non-attribution baseline) Rep-Sim computes the cosine similarity between last29 token last layer hidden states of training and reference examples. It is more efficient compared with30 gradient-based data attribution methods. BM25 [48]: (Non-attribution baseline) BM25 is a classic information retrieval algorithm that ranks33 training samples by lexical overlap with the query. It is significantly more efficient compared with34 gradient-based data attribution methods.35

Data attribution methods quantify the influence of training data on model outputs and are becoming increasingly relevant for a wide range of LLM research and applications, including dataset curation, model interpretability, data valuation. However, there remain critical gaps in systematic LLM-centric evaluation of data attribution methods. To this end, we introduce DATE-LM (Data Attribution Evaluation in Language Models), a unified benchmark for evaluating data attribution methods through real-world LLM applications.

Measuring progress is fundamental to the advancement of any scientific field. As benchmarks play an increasingly central role, they also become more susceptible to distortion. Chatbot Arena has emerged as the go-to leaderboard for ranking the most capable AI systems. Yet, in this work we identify systematic issues that have skewed the competitive landscape. Specifically, undisclosed private testing practices benefit a handful of providers who are able to test multiple variants before public release and selectively retract scores.

The meteoric rise of Artificial Intelligence (AI), with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals and erodes public confidence, much as unchecked claims would destabilize financial markets reliant on credible oversight from agencies like Moody's. In high-stakes human examinations (e.g., SAT, GRE), substantial effort is devoted to ensuring fairness and credibility; why settle for less in evaluating AI, especially given its profound societal impact? This position paper argues that a laissezfaire approach is untenable. For true and sustainable AI advancement, we call for a paradigm shift to a unified, live, and quality-controlled benchmarking framework--robust by construction rather than reliant on courtesy or goodwill.

Measuring progress is fundamental to the advancement of any scientific field. As benchmarks play an increasingly central role, they also grow more susceptible to distortion.Chatbot Arena has emerged as the go-to leaderboard for ranking the most capable AI systems. Yet, in this work we identify systematic issues that have resulted in a distorted playing field. We find that undisclosed private testing practices benefit a handful of providers who are able to test multiple variants before public release and retract scores if desired. We establish that the ability of these providers to choose the best score leads to biased Arena scores due to selective disclosure of performance results.

Differentially private (DP) synthetic data generation is a promising technique for utilizing private datasets that otherwise cannot be exposed for model training or other analytics. While much research literature has focused on generating private unstructured text and image data, in enterprise settings, structured data (e.g., tabular) is more common, often including natural language fields or components. Existing synthetic data evaluation techniques (e.g., FID) struggle to capture the structural properties and correlations of such datasets. In this work, we propose Struct-Bench, a framework and benchmark for evaluating synthetic datasets derived from structured datasets that contain natural language data. The Struct-Bench framework requires users to provide a representation of their dataset structure as a Context-Free Grammar (CFG).

We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform. Our platform evaluates NLP models directly instead of relying on selfreported metrics or predictions on a single dataset. Under this paradigm, models are submitted to be evaluated in the cloud, circumventing the issues of reproducibility, accessibility, and backwards compatibility that often hinder benchmarking in NLP. This allows users to interact with uploaded models in real time to assess their quality, and permits the collection of additional metrics such as memory use, throughput, and robustness, which - despite their importance to practitioners - have traditionally been absent from leaderboards. On each task, models are ranked according to the Dynascore, a novel utility-based aggregation of these statistics, which users can customize to better reflect their preferences, placing more/less weight on a particular axis of evaluation or dataset. As state-of-the-art NLP models push the limits of traditional benchmarks, Dynaboard offers a standardized solution for a more diverse and comprehensive evaluation of model quality.

With the rapid development of code LLMs, many popular evaluation benchmarks, such as HumanEval, DS-1000, and MBPP, have emerged to measure the performance of code LLMs with a particular focus on code generation tasks. However, they are insufficient to cover the full range of expected capabilities of code LLMs, which span beyond code generation to answering diverse coding-related questions.

In machine learning research, it is common to evaluate algorithms via their performance on standard benchmark datasets.