United States

Large Language Models (LLMs) are trained on web-scale corpora, increasing the risk that benchmark test data appears in training sets and inflates reported performance. We present a systematic literature review of 55 studies on LLM benchmark contamination through late 2025. Our contributions are: (1) a four-tier contamination taxonomy (Exact, Syntactic, Semantic, Task-Level; T1–T4); (2) a comparative analysis of five detection families (string-matching, likelihood-based, membership inference, LLM-prompted detection, and benchmark auditing), including access assumptions and failure modes; (3) a synthesis of contamination evidence on MMLU, GSM8K, HumanEval, and HellaSwag by measurement construct; (4) a comparative evaluation of mitigation strategies across lifecycle points, access assumptions, and evidence maturity; and (5) a Contamination Transparency Card (CTC) framework for future releases. Across studies, no detection method is consistently reliable across contamination tiers, model-access settings, and training stages. We identify instruction tuning as a persistent blind spot, note that RL/post-training contamination auditing is only beginning to mature, and report inflation estimates spanning roughly 6%–40% under benchmark- and setting-dependent assumptions.

ACL 2026

Are LLM Benchmarks Already Contaminated? A Systematic Review of Contamination Detection Methods

training data contamination

benchmark evaluation integrity

data leakage detection

llm benchmark contamination

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

workshop paper

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Language models (LMs) are known to be prone to response biases, which present as option preference biases in fixed-response questions. It is therefore imperative to develop low-cost and effective response bias correction methods to improve LM performance and enable more accurate evaluations of model abilities. Here, we propose a simple response bias correction strategy, $\texttt{RBCorr}$, and test it on 12 open-weight language models using yes-no, entailment, and multiple choice questions. We show that response bias is prevalent in LMs pre-correction and that $\texttt{RBCorr}$ effectively eliminates bias and boosts model performance. We also explore the generalizability of bias behavior across models, datasets, and prompt formats, showing that LogProbs-based correction is highly dependent on all three of these aspects. Overall, $\texttt{RBCorr}$ is an easy-to-use method that can boost the performance of smaller LMs and ensure that LM performance on closed-response benchmarks aligns more closely with their true capabilities.

RBCorr: Response Bias Correction in Language Models

Tokenization is central to modern language models, yet its effects on cross-script efficiency, input cost, and truncation behavior remain underexplored. We study this issue through aligned comparisons of Urdu and Roman Urdu, asking whether semantically equivalent content incurs systematically different tokenization costs across scripts. We introduce Token Cost Inequality (TCI), a metric for quantifying relative tokenization efficiency under semantic alignment, and propose a multi-axis framework spanning token cost, fragmentation, and fixed-budget retention. Across three tokenizer families (cl100k, mT5, and ByT5), we find that tokenization disparities are strongly tokenizer-dependent, with substantial differences in token cost and segmentation behavior across scripts. We further identify an efficiency-retention paradox: token cost alone does not fully explain truncation behavior. Under fixed token budgets, Roman Urdu preserves more character-level content than native Urdu, reflecting differences in character-per-token density and fragmentation. Lightweight normalization yields minimal gains, suggesting that the observed disparities arise primarily from tokenizer design rather than superficial orthographic variation. These findings provide controlled evidence that fixed token budgets can produce unequal surface-coverage conditions across scripts, with implications for input-side cost estimation, benchmark design, and multilingual evaluation under constrained token budgets.

Token Cost Inequality: Measuring Tokenization Disparities Across Scripts in Roman Urdu and Urdu

Reference-free evaluation of LLM-generated code is essential when execution-based testing is unavailable or costly. We compare two paradigms: $\textbf{explicit LLM-as-a-Judge}$ scoring, which assigns a quality score to a solution, and $\textbf{log-probability scoring}$, which uses $\log P_\theta(\text{code} \mid \text{task})$ as an instruction-free signal. Across HumanEval-X, we find that the two approaches capture $\textit{qualitatively different aspects}$ of code correctness. Explicit judges — particularly larger models — perform strongly on generated code, reflecting their ability to reason about task-solution alignment, but fail to distinguish correct solutions from minimally mutated ones. Log-probability exhibits the opposite pattern: weaker performance on generated code, but consistent pairwise separation of canonical from mutated solutions. These results reveal a $\textbf{discrimination-ranking dissociation}$ and show that the two paradigms provide complementary, non-interchangeable signals: explicit judges capture semantic correctness, while log-probability captures local structural consistency.

Semantic vs. Structural Signals: Log-Probability and LLM-as-a-Judge for Reference-Free Code Evaluation

LLM-as-judge evaluation is widely used in benchmarking pipelines, where model outputs are compared and ranked using automated evaluators. These pipelines typically assume that judgments are stable properties of fixed inputs. We show that this assumption does not hold under interaction. We study post-decision manipulability: the extent to which an evaluation outcome can be altered through subsequent conversation with the judge after an initial decision has been made. Across controlled experiments on MT-Bench and AlpacaEval, we find that LLM judges are highly stable under repeated and neutral reevaluation, yet become substantially reversible under targeted post-decision challenge. An anti-baseline challenge protocol shows that stable judgments can be overturned through motivated interaction, while a counterbalanced target-validation protocol separates this reversibility from net target-directed steering. These reversals have practical consequences: they can degrade agreement with human preferences, shift benchmark rankings, and produce harmful evaluation changes despite high self-reported confidence. Authority framing is especially destabilizing, and revised judgments are often accompanied by low-overlap justifications, suggesting post hoc rationalization rather than reliable error correction. We introduce the Evaluation Robustness Score (ERS) to quantify interactional robustness by combining reversal susceptibility with counterbalanced directional effects. Our findings identify post-decision interaction as a distinct failure mode for LLM-as-judge evaluation and motivate evaluation protocols that measure not only static agreement, but robustness under challenge.

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges

Large language models (LLMs) are now widely used as judges, yet their decisions can change under presentation choices that should be irrelevant. We study one such source of instability: candidate-order sensitivity in listwise factuality evaluation, where several answers can look similarly polished while differing substantially in hallucination risk. We introduce PCFJudge, an inference-time method that reruns the same factuality-first listwise prompt over multiple orderings of the same candidate set and aggregates the resulting scores, ranks, and uncertainty signals into a single consensus decision. On RewardBench 2 Factuality, the final seven-permutation aggregate (K=7) improves top-1 selection accuracy from 86.00\% to 91.33\% with GPT-5.4 and from 86.33\% to 89.67\% with Claude Sonnet 4.6. These results suggest that candidate order can be a meaningful source of factuality-judging error and that marginalizing over this nuisance variation can improve the reliability of LLM evaluation.

Permutation-Consensus Listwise Judging for Robust Factuality Evaluation

Deploying Large Language Models (LLMs) in medical applications requires rigorous fact-checking to ensure patient safety and regulatory compliance. We introduce **MedFact**, a challenging Chinese medical fact-checking benchmark with 2,116 expert-annotated instances from diverse real-world texts, spanning 13 specialties, 8 error types, 4 writing styles, and 5 difficulty levels. Construction uses a hybrid AI-human framework where iterative expert feedback refines AI-driven, multi-criteria filtering to ensure high quality and difficulty. We evaluate 20 leading LLMs on veracity classification and error localization, and results show that models can often determine whether text contains errors but struggle to localize them precisely, with top performers falling short of human performance. Our analysis reveals an "over-criticism" phenomenon, where models misidentify correct information as erroneous, a tendency that is aggravated by advanced reasoning techniques such as multi-agent collaboration and inference-time scaling. MedFact highlights the challenges of deploying medical LLMs and provides resources to develop factually reliable medical AI systems.

MedFact: Benchmarking the Fact-Checking Capabilities of Large Language Models on Chinese Medical Texts

Evaluating reasoning quality in multi-agent LLM systems is challenging, especially for open-ended tasks without reference answers. We investigate whether intrinsic confidence signals, token-level log-probabilities from decoding, can predict reasoning quality as assessed by LLM-as-judge evaluation. Using a debate-based essay scoring framework, we compare confidence proxies against rubric-based judge scores across two ASAP essay sets. We find that early-token confidence, particularly within the first few generated tokens, is consistently the strongest predictor of reasoning quality, outperforming full-sequence statistics. Analysis of log-probability trajectories shows that the opening phase of generation is the most heterogeneous and therefore most informative. We also observe a systematic asymmetry between agent roles, with stronger alignment between confidence and quality for supportive reasoning than for adversarial critique. These results suggest that early decoding dynamics provide a lightweight and effective signal for estimating reasoning reliability in multi-agent LLM systems.

Early-Token Confidence Predicts Reasoning Quality in Multi-Agent LLM Debate

Large language models (LLMs) are increasingly used as judges of chain-of-thought (CoT) reasoning, yet it remains unclear whether they can reliably assess process faithfulness rather than merely answer plausibility. We introduce C2-Faith, a benchmark built from PRM800K that explicitly decomposes faithfulness into two complementary dimensions: causality (whether each step logically follows from prior context) and coverage (whether essential intermediate inferences are present). Using controlled perturbations, we construct examples with known causal error positions by replacing a single step with a logically inconsistent variant, and with controlled coverage deletions at varying rates, enabling direct measurement against reference labels. We evaluate three frontier LLM judges across three tasks: binary causal detection, causal step localization, and coverage scoring. Our results reveal that judge reliability is highly task-dependent, with no single model dominating across settings. While models often detect that an error exists, they struggle to accurately localize it, indicating a substantial gap between detection and attribution. Moreover, all judges systematically overestimate reasoning completeness, assigning high coverage scores even when substantial portions of intermediate reasoning are missing. These findings expose fundamental limitations of LLM judges in process-level evaluation and highlight the need for more reliable and calibrated methods when using LLMs to assess reasoning quality.

C2-Faith: Benchmarking LLM Judges for Causal and Coverage Faithfulness in Chain-of-Thought Reasoning

We present a multilingual study of sentiment evaluation on Wikipedia articles from various topics in five languages (German, English, Spanish, Polish, and Russian). In this paper, we compare three large language models (Gemini Pro 3.1, Claude Opus 4.6, and GPT 5.2), each queried three times per sentence, with two popular multilingual sentiment classifiers. This setup allows us to analyze not only inter-model differences but also intra-model stability as a proxy for confidence. To support systematic evaluation, we construct a benchmark dataset based on strict consensus across evaluators and analyze sentiment distributions across topics and languages. We show substantial variation in sentiment distributions, agreement, and consistency across models and languages. Our results suggest that sentiment evaluation on encyclopedic text remains an underexplored challenge for multilingual NLP.

Evaluating Multilingual Sentiment Classifiers Using an LLM-Annotated Wikipedia Benchmark

Recent advances in language modeling have led to a growing number of papers related to finance in top-tier Natural Language Processing (NLP) venues. To systematically examine this trend, we review 374 NLP research papers published between 2017 and 2024 across 38 conferences and workshops, with a focused analysis of 221 papers that directly address finance-related tasks. We evaluate these papers across 11 quantitative and qualitative dimensions, with particular attention to evaluation practices, metric choices, dataset coverage, and reproducibility in a high-stakes applied LM domain. Our study identifies the following opportunities for NLP researchers: (i) expanding the scope of forecasting tasks; (ii) enriching evaluation with finance-specific metrics; (iii) leveraging multilingual and crisis-period datasets for robustness-oriented evaluation; and (iv) balancing PLMs with efficient or interpretable alternatives. We identify actionable directions supported by dataset and tool recommendations, with implications for both academic evaluation practices and industry deployment.

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